<?xml version="1.0"?>
<doc>
    <assembly>
        <name>Microsoft.ML.StandardLearners</name>
    </assembly>
    <members>
        <member name="T:Microsoft.ML.FactorizationMachineExtensions">
            <summary>
            Extension method to create <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer"/>
            </summary>
        </member>
        <member name="M:Microsoft.ML.FactorizationMachineExtensions.FieldAwareFactorizationMachine(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String[],System.String,System.String)">
            <summary>
            Predict a target using a field-aware factorization machine algorithm.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="featureColumnNames">The name(s) of the feature columns.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[FieldAwareFactorizationMachine](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachine.cs)]
            ]]></format>
            </example>
        </member>
        <member name="M:Microsoft.ML.FactorizationMachineExtensions.FieldAwareFactorizationMachine(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options)">
            <summary>
            Predict a target using a field-aware factorization machine algorithm.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[FieldAwareFactorizationMachine](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/FieldAwareFactorizationMachineWithOptions.cs)]
            ]]></format>
            </example>
        </member>
        <member name="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer">
            <summary>
        Train a field-aware factorization machine for binary classification using ADAGRAD (an advanced stochastic gradient method). 
      </summary><remarks>
        Field Aware Factorization Machines use, in addition to the input variables, factorized parameters to model the interaction between pairs of variables.
        The algorithm is particularly useful for high dimensional datasets which can be very sparse (for example, click-prediction for advertising systems).
        <para>An advantage of FFM over SVMs is that the training data does not need to be stored in memory, and the coefficients can be optimized directly.
          For a general idea of what Field-aware Factorization Machines are see: <a href="https://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf">Field Aware Factorization Machines</a>
        </para>
        <para>See references below for more details. 
        This trainer is essentially faster the one introduced in [2] because of some implemtation tricks[3].
        </para>
          <list type="bullet">
            <item>
              <description><a href="https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf">Field-aware Factorization Machines for CTR Prediction</a></description></item>
            <item>
              <description><a href="http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></description>
            </item>
            <item>
              <description><a href="https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf">An Improved Stochastic Gradient Method for Training Large-scale Field-aware Factorization Machine.</a></description>
            </item>
          </list>
      </remarks>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.LearningRate">
            <summary>
            Initial learning rate.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.NumberOfIterations">
            <summary>
            Number of training iterations.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.LatentDimension">
            <summary>
            Latent space dimension.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.LambdaLinear">
            <summary>
            Regularization coefficient of linear weights.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.LambdaLatent">
            <summary>
            Regularization coefficient of latent weights.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.Normalize">
            <summary>
            Whether to normalize the input vectors so that the concatenation of all fields' feature vectors is unit-length.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.ExtraFeatureColumns">
            <summary>
            Extra feature column names. The column named <see cref="F:Microsoft.ML.EntryPoints.LearnerInputBase.FeatureColumn"/> stores features from the first field.
            The i-th string in <see cref="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.ExtraFeatureColumns"/> stores the name of the (i+1)-th field's feature column.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.Shuffle">
            <summary>
            Whether to shuffle for each training iteration.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.Verbose">
            <summary>
            Report traning progress or not.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options.Radius">
            <summary>
            Radius of initial latent factors.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.FeatureColumns">
            <summary>
            The feature column that the trainer expects.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.LabelColumn">
            <summary>
            The label column that the trainer expects. Can be <c>null</c>, which indicates that label
            is not used for training.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.WeightColumn">
            <summary>
            The weight column that the trainer expects. Can be <c>null</c>, which indicates that weight is
            not used for training.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Microsoft#ML#ITrainer#Info">
            <summary>
            The <see cref="T:Microsoft.ML.TrainerInfo"/> containing at least the training data for this trainer.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer"/> through the <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options"/> class.
            </summary>
            <param name="env">The private instance of <see cref="T:Microsoft.ML.IHostEnvironment"/>.</param>
            <param name="options">An instance of the legacy <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options"/> to apply advanced parameters to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String[],System.String,System.String)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer"/>.
            </summary>
            <param name="env">The private instance of <see cref="T:Microsoft.ML.IHostEnvironment"/>.</param>
            <param name="featureColumnNames">The name of column hosting the features. The i-th element stores feature column of the i-th field.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="weightColumnName">The name of the weight column (optional).</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Initialize(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Options)">
            <summary>
            Initializes the instance. Shared between the two constructors.
            REVIEW: Once the legacy constructor goes away, this can move to the only constructor and most of the fields can be back to readonly.
            </summary>
            <param name="env"></param>
            <param name="options"></param>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Fit(Microsoft.Data.DataView.IDataView,Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer"/> using an already trained <paramref name="modelParameters"/> and/or validation data,
            and returns a <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.Fit(Microsoft.Data.DataView.IDataView)">
            <summary> Trains and returns a <see cref="T:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer"/>.</summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineBinaryClassificationTrainer.GetOutputSchema(Microsoft.ML.SchemaShape)">
            <summary>
            Schema propagation for transformers. Returns the output schema of the data, if
            the input schema is like the one provided.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.FieldCount">
            <summary>
            Get the number of fields. It's the symbol `m` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.FeatureCount">
            <summary>
            Get the number of features. It's the symbol `n` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.LatentDimension">
            <summary>
            Get the latent dimension. It's the tlngth of `v_{j, f}` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Boolean,System.Int32,System.Int32,System.Int32,System.Single[],System.Single[])">
            <summary>
            Initialize model parameters with a trained model.
            </summary>
            <param name="env">The host environment</param>
            <param name="norm">True if user wants to normalize feature vector to unit length.</param>
            <param name="fieldCount">The number of fileds, which is the symbol `m` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf </param>
            <param name="featureCount">The number of features, which is the symbol `n` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf </param>
            <param name="latentDim">The latent dimensions, which is the length of `v_{j, f}` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf </param>
            <param name="linearWeights">The linear coefficients of the features, which is the symbol `w` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf </param>
            <param name="latentWeights">Latent representation of each feature. Note that one feature may have <see cref="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.FieldCount"/> latent vectors
            and each latent vector contains <see cref="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.LatentDimension"/> values. In the f-th field, the j-th feature's latent vector, `v_{j, f}` in the doc
            https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf, starts at latentWeights[j * fieldCount * latentDim + f * latentDim].
            The k-th element in v_{j, f} is latentWeights[j * fieldCount * latentDim + f * latentDim + k]. The size of the array must be featureCount x fieldCount x latentDim.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.GetLinearWeights">
            <summary>
            The linear coefficients of the features. It's the symbol `w` in the doc: https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.GetLatentWeights">
            <summary>
            Latent representation of each feature. Note that one feature may have <see cref="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.FieldCount"/> latent vectors
            and each latent vector contains <see cref="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachineModelParameters.LatentDimension"/> values. In the f-th field, the j-th feature's latent vector, `v_{j, f}` in the doc
            https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf, starts at latentWeights[j * fieldCount * latentDim + f * latentDim].
            The k-th element in v_{j, f} is latentWeights[j * fieldCount * latentDim + f * latentDim + k].
            The size of the returned value is featureCount x fieldCount x latentDim.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer.FeatureColumns">
            <summary>
            The name of the feature column used by the prediction transformer.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer.FeatureColumnTypes">
            <summary>
            The type of the feature columns.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer.GetOutputSchema(Microsoft.Data.DataView.DataViewSchema)">
            <summary>
            Gets the <see cref="T:Microsoft.Data.DataView.DataViewSchema"/> result after transformation.
            </summary>
            <param name="inputSchema">The <see cref="T:Microsoft.Data.DataView.DataViewSchema"/> of the input data.</param>
            <returns>The post transformation <see cref="T:Microsoft.Data.DataView.DataViewSchema"/>.</returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachinePredictionTransformer.SaveModel(Microsoft.ML.ModelSaveContext)">
            <summary>
            Saves the transformer to file.
            </summary>
            <param name="ctx">The <see cref="T:Microsoft.ML.ModelSaveContext"/> that facilitates saving to the <see cref="T:Microsoft.ML.Repository"/>.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.FieldAwareFactorizationMachineScalarRowMapper.Microsoft#ML#Data#ISchemaBoundRowMapper#GetDependenciesForNewColumns(System.Collections.Generic.IEnumerable{Microsoft.Data.DataView.DataViewSchema.Column})">
            <summary>
            Given a set of columns, return the input columns that are needed to generate those output columns.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.LinearModelParameters.Weights">
            <summary> The predictor's feature weight coefficients.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.LinearModelParameters.Bias">
            <summary> The predictor's bias term.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.LinearModelParameters.Microsoft#ML#Model#ICalculateFeatureContribution#FeatureContributionCalculator">
            <summary>
            Used to determine the contribution of each feature to the score of an example by <see cref="T:Microsoft.ML.Transforms.FeatureContributionCalculatingTransformer"/>.
            For linear models, the contribution of a given feature is equal to the product of feature value times the corresponding weight.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.String,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single)">
            <summary>
            Constructs a new linear predictor.
            </summary>
            <param name="env">The host environment.</param>
            <param name="name">Component name.</param>
            <param name="weights">The weights for the linear model. The i-th element of weights is the coefficient
            of the i-th feature. Note that this will take ownership of the <see cref="T:Microsoft.ML.Data.VBuffer`1"/>.</param>
            <param name="bias">The bias added to every output score.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearModelParameters.CombineParameters(System.Collections.Generic.IList{Microsoft.ML.Model.IParameterMixer{System.Single}},Microsoft.ML.Data.VBuffer{System.Single}@,System.Single@)">
            <summary>
            Combine a bunch of models into one by averaging parameters
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearBinaryModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.Trainers.LinearModelStatistics)">
            <summary>
            Constructs a new linear binary predictor.
            </summary>
            <param name="env">The host environment.</param>
            <param name="weights">The weights for the linear model. The i-th element of weights is the coefficient
            of the i-th feature. Note that this will take ownership of the <see cref="T:Microsoft.ML.Data.VBuffer`1"/>.</param>
            <param name="bias">The bias added to every output score.</param>
            <param name="stats"></param>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearBinaryModelParameters.Microsoft#ML#Model#IParameterMixer{System#Single}#CombineParameters(System.Collections.Generic.IList{Microsoft.ML.Model.IParameterMixer{System.Single}})">
            <summary>
            Combine a bunch of models into one by averaging parameters
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearBinaryModelParameters.Microsoft#ML#Model#ICanGetSummaryInKeyValuePairs#GetSummaryInKeyValuePairs(Microsoft.ML.Data.RoleMappedSchema)">
            <inheritdoc/>
        </member>
        <member name="M:Microsoft.ML.Trainers.RegressionModelParameters.SaveAsIni(System.IO.TextWriter,Microsoft.ML.Data.RoleMappedSchema,Microsoft.ML.Calibrators.ICalibrator)">
            <summary>
            Output the INI model to a given writer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearRegressionModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single)">
            <summary>
            Constructs a new linear regression model from trained weights.
            </summary>
            <param name="env">The host environment.</param>
            <param name="weights">The weights for the linear model. The i-th element of weights is the coefficient
            of the i-th feature. Note that this will take ownership of the <see cref="T:Microsoft.ML.Data.VBuffer`1"/>.</param>
            <param name="bias">The bias added to every output score.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearRegressionModelParameters.Microsoft#ML#Model#IParameterMixer{System#Single}#CombineParameters(System.Collections.Generic.IList{Microsoft.ML.Model.IParameterMixer{System.Single}})">
            <summary>
            Combine a bunch of models into one by averaging parameters
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearRegressionModelParameters.Microsoft#ML#Model#ICanGetSummaryInKeyValuePairs#GetSummaryInKeyValuePairs(Microsoft.ML.Data.RoleMappedSchema)">
            <inheritdoc/>
        </member>
        <member name="M:Microsoft.ML.Trainers.PoissonRegressionModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single)">
            <summary>
            Constructs a new Poisson regression model parameters from trained model.
            </summary>
            <param name="env">The Host environment.</param>
            <param name="weights">The weights for the linear model. The i-th element of weights is the coefficient
            of the i-th feature. Note that this will take ownership of the <see cref="T:Microsoft.ML.Data.VBuffer`1"/>.</param>
            <param name="bias">The bias added to every output score.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.PoissonRegressionModelParameters.Microsoft#ML#Model#IParameterMixer{System#Single}#CombineParameters(System.Collections.Generic.IList{Microsoft.ML.Model.IParameterMixer{System.Single}})">
            <summary>
            Combine a bunch of models into one by averaging parameters
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.LinearPredictorUtils">
            <summary>
            Helper methods for linear predictors
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearPredictorUtils.SaveAsCode(System.IO.TextWriter,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.Data.RoleMappedSchema,System.String)">
            <summary>
            print the linear model as code
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearPredictorUtils.FeatureNameAsCode(System.String,System.Int32)">
            <summary>
            Ensure that feature name is a legitimate variable name
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearPredictorUtils.LinearModelAsIni(Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.IPredictor,Microsoft.ML.Data.RoleMappedSchema,Microsoft.ML.Calibrators.PlattCalibrator)">
            <summary>
            Build a Bing TreeEnsemble .ini representation of the given predictor
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearPredictorUtils.LinearModelAsText(System.String,System.String,System.String,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.Data.RoleMappedSchema,Microsoft.ML.Calibrators.PlattCalibrator)">
            <summary>
            Output the weights of a linear model to a given writer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearPredictorUtils.SaveLinearModelWeightsInKeyValuePairs(Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.Data.RoleMappedSchema,System.Collections.Generic.List{System.Collections.Generic.KeyValuePair{System.String,System.Object}})">
            <summary>
            Output the weights of a linear model to key value pairs.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.LbfgsTrainerBase`3.OptionsBase.Quiet">
            <summary>
            Features must occur in at least this many instances to be included
            </summary>
            <remarks>If greater than 1, forces an initialization pass over the data</remarks>
        </member>
        <member name="F:Microsoft.ML.Trainers.LbfgsTrainerBase`3.OptionsBase.InitWtsDiameter">
            <summary>
            Init Weights Diameter
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.LbfgsTrainerBase`3.OptionsBase.NumThreads">
            <summary>
            Number of threads. Null means use the number of processors.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LbfgsTrainerBase`3.InitializeWeightsSgd(Microsoft.ML.IChannel,Microsoft.ML.Trainers.FloatLabelCursor.Factory)">
            <summary>
            Initialize weights by running SGD up to specified tolerance.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LbfgsTrainerBase`3.TrainModelCore(Microsoft.ML.TrainContext)">
            <summary>
            The basic training calls the optimizer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LbfgsTrainerBase`3.DifferentiableFunction(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.IProgressChannelProvider)">
            <summary>
            The gradient being used by the optimizer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LbfgsTrainerBase`3.DifferentiableFunctionMultithreaded(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.IProgressChannel)">
            <summary>
            Batch-parallel optimizer
            </summary>
            <remarks>
            REVIEW: consider getting rid of multithread-targeted members
            Using TPL, the distinction between Multithreaded and Sequential implementations is unnecessary
            </remarks>
        </member>
        <member name="T:Microsoft.ML.Trainers.LogisticRegression">
            <summary>
        Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as 
        a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.
      </summary><remarks>
        If the dependent variable has more than two possible values (blood type given diagnostic test results), 
        then the logistic regression is multinomial.
        <para>
          The optimization technique used for LogisticRegression Classifier is based on the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS).
          Both the L-BFGS and regular BFGS algorithms use quasi-Newtonian methods to estimate the computationally intensive 
          Hessian matrix in the equation used by Newton's method to calculate steps.
          But the L-BFGS approximation uses only a limited amount of memory to compute the next step direction,
          so that it is especially suited for problems with a large number of variables.
          The MemorySize argument specifies the number of past positions and gradients to store for use in the 
          computation of the next step.
        </para>
        <para>
          This learner can use elastic net regularization: a linear combination of L1 (LASSO) and L2 (ridge) regularizations.
          Regularization is a method that can render an ill-posed problem more tractable by imposing constraints that provide information 
          to supplement the data and that prevents overfitting by penalizing models with extreme coefficient values.
          This can improve the generalization of the model learned by selecting the optimal complexity in the bias-variance tradeoff.
          Regularization works by adding the penalty that is associated with coefficient values to the error of the hypothesis.
          An accurate model with extreme coefficient values would be penalized more, but a less accurate model with more conservative 
          values would be penalized less. L1 and L2 regularization have different effects and uses that are complementary in certain respects.
        </para>
          <list type="bullet">
            <item><description>
              L1Weight can be applied to sparse models, when working with high-dimensional data. It pulls small weights associated features
              that are relatively unimportant towards 0.
              L1 regularization is an implementation of OWLQN, based on:
              <a href="https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.5260">Scalable training of L1-regularized log-linear models</a>
            </description></item>
            <item><description>L2Weight is preferable for data that is not sparse. It pulls large weights towards zero.</description></item>
          </list>
          Adding the ridge penalty to the regularization overcomes some of lasso's limitations. It can improve its predictive accuracy, for example, when the number of predictors is greater than the sample size. If x = l1_weight and y = l2_weight, ax + by = c defines the linear span of the regularization terms.
          The default values of x and y are both 1.
          An agressive regularization can harm predictive capacity by excluding important variables out of the model. So choosing the optimal values for the regularization parameters is important for the performance of the logistic regression model.
        <para>For more information see:</para>
        <list type="bullet">
          <item><description><a href="https://research.microsoft.com/apps/pubs/default.aspx?id=78900">Scalable Training of L1-Regularized Log-Linear Models</a>.</description></item>
          <item><description><a href="https://msdn.microsoft.com/en-us/magazine/dn904675.aspx">Test Run - L1 and L2 Regularization for Machine Learning</a>.</description></item>
          <item><description><a href="https://en.wikipedia.org/wiki/L-BFGS">Wikipedia: L-BFGS</a>.</description></item>
          <item><description><a href="https://en.wikipedia.org/wiki/Logistic_regression">Wikipedia: Logistic regression</a>.</description></item>
        </list>
      </remarks>
            <!-- No matching elements were found for the following include tag --><include file="doc.xml" path="docs/members/example[@name=&quot;LogisticRegressionBinaryClassifier&quot;]/*" />
            <summary>
            A component to train a logistic regression model.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.LogisticRegression.Options.ShowTrainingStats">
            <summary>
            If set to <value>true</value>training statistics will be generated at the end of training.
            If you have a large number of learned training parameters(more than 500),
            generating the training statistics might take a few seconds.
            More than 1000 weights might take a few minutes. For those cases consider using the instance of <see cref="T:Microsoft.ML.Trainers.ComputeLRTrainingStd"/>
            present in the Microsoft.ML.HalLearners package. That computes the statistics using hardware acceleration.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.LogisticRegression.Options.StdComputer">
            <summary>
            The instance of <see cref="T:Microsoft.ML.Trainers.ComputeLRTrainingStd"/> that computes the std of the training statistics, at the end of training.
            The calculations are not part of Microsoft.ML package, due to the size of MKL.
            If you need these calculations, add the Microsoft.ML.HalLearners package, and initialize <see cref="F:Microsoft.ML.Trainers.LogisticRegression.Options.StdComputer"/>.
            to the <see cref="T:Microsoft.ML.Trainers.ComputeLRTrainingStd"/> implementation in the Microsoft.ML.HalLearners package.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LogisticRegression.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The name of the label column.</param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="weights">The name for the example weight column.</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
            <param name="l1Weight">Weight of L1 regularizer term.</param>
            <param name="l2Weight">Weight of L2 regularizer term.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.LogisticRegression.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.LogisticRegression.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LogisticRegression.Fit(Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.LinearModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/> using an already trained <paramref name="modelParameters"/> and returns
            a <see cref="T:Microsoft.ML.Data.BinaryPredictionTransformer`1"/>.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.ComputeLRTrainingStd">
            <summary>
            Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation,
            p-value and z-Score.
            If you need fast calculations, use the <see cref="T:Microsoft.ML.Trainers.ComputeLRTrainingStd"/> implementation in the Microsoft.ML.HALLearners package,
            which makes use of hardware acceleration.
            Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.ComputeLRTrainingStd.ComputeStd(System.Double[],System.Int32[],System.Int32,System.Int32,Microsoft.ML.IChannel,System.Single)">
            <summary>
            Computes the standard deviation matrix of each of the non-zero training weights, needed to calculate further the standard deviation,
            p-value and z-Score.
            The calculations are not part of Microsoft.ML package, due to the size of MKL.
            If you need these calculations, add the Microsoft.ML.HalLearners package, and initialize <see cref="F:Microsoft.ML.Trainers.LogisticRegression.Options.StdComputer"/>
            to the <see cref="T:Microsoft.ML.Trainers.ComputeLRTrainingStd"/> implementation in the Microsoft.ML.HalLearners package.
            Due to the existence of regularization, an approximation is used to compute the variances of the trained linear coefficients.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.MulticlassLogisticRegression">
            <summary>
        Logistic Regression is a method in statistics used to predict the probability of occurrence of an event and can be used as 
        a classification algorithm. The algorithm predicts the probability of occurrence of an event by fitting data to a logistical function.
      </summary><remarks>
        If the dependent variable has more than two possible values (blood type given diagnostic test results), 
        then the logistic regression is multinomial.
        <para>
          The optimization technique used for LogisticRegression Classifier is based on the limited memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS).
          Both the L-BFGS and regular BFGS algorithms use quasi-Newtonian methods to estimate the computationally intensive 
          Hessian matrix in the equation used by Newton's method to calculate steps.
          But the L-BFGS approximation uses only a limited amount of memory to compute the next step direction,
          so that it is especially suited for problems with a large number of variables.
          The MemorySize argument specifies the number of past positions and gradients to store for use in the 
          computation of the next step.
        </para>
        <para>
          This learner can use elastic net regularization: a linear combination of L1 (LASSO) and L2 (ridge) regularizations.
          Regularization is a method that can render an ill-posed problem more tractable by imposing constraints that provide information 
          to supplement the data and that prevents overfitting by penalizing models with extreme coefficient values.
          This can improve the generalization of the model learned by selecting the optimal complexity in the bias-variance tradeoff.
          Regularization works by adding the penalty that is associated with coefficient values to the error of the hypothesis.
          An accurate model with extreme coefficient values would be penalized more, but a less accurate model with more conservative 
          values would be penalized less. L1 and L2 regularization have different effects and uses that are complementary in certain respects.
        </para>
          <list type="bullet">
            <item><description>
              L1Weight can be applied to sparse models, when working with high-dimensional data. It pulls small weights associated features
              that are relatively unimportant towards 0.
              L1 regularization is an implementation of OWLQN, based on:
              <a href="https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.68.5260">Scalable training of L1-regularized log-linear models</a>
            </description></item>
            <item><description>L2Weight is preferable for data that is not sparse. It pulls large weights towards zero.</description></item>
          </list>
          Adding the ridge penalty to the regularization overcomes some of lasso's limitations. It can improve its predictive accuracy, for example, when the number of predictors is greater than the sample size. If x = l1_weight and y = l2_weight, ax + by = c defines the linear span of the regularization terms.
          The default values of x and y are both 1.
          An agressive regularization can harm predictive capacity by excluding important variables out of the model. So choosing the optimal values for the regularization parameters is important for the performance of the logistic regression model.
        <para>For more information see:</para>
        <list type="bullet">
          <item><description><a href="https://research.microsoft.com/apps/pubs/default.aspx?id=78900">Scalable Training of L1-Regularized Log-Linear Models</a>.</description></item>
          <item><description><a href="https://msdn.microsoft.com/en-us/magazine/dn904675.aspx">Test Run - L1 and L2 Regularization for Machine Learning</a>.</description></item>
          <item><description><a href="https://en.wikipedia.org/wiki/L-BFGS">Wikipedia: L-BFGS</a>.</description></item>
          <item><description><a href="https://en.wikipedia.org/wiki/Logistic_regression">Wikipedia: Logistic regression</a>.</description></item>
        </list>
      </remarks>
            <!-- No matching elements were found for the following include tag --><include file="doc.xml" path="docs/members/example[@name=&quot;LogisticRegressionClassifier&quot;]/*" />
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegression.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The name of the label column.</param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="weights">The name for the example weight column.</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
            <param name="l1Weight">Weight of L1 regularizer term.</param>
            <param name="l2Weight">Weight of L2 regularizer term.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegression.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.MulticlassLogisticRegression.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/>
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegression.Fit(Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/> using an already trained <paramref name="modelParameters"/> and returns
            a <see cref="T:Microsoft.ML.Data.MulticlassPredictionTransformer`1"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Int32,System.Int32,System.String[],Microsoft.ML.Trainers.LinearModelStatistics)">
            <summary>
            Initializes a new instance of the <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters"/> class.
            This constructor is called by <see cref="T:Microsoft.ML.Trainers.SdcaMultiClassTrainer"/> to create the predictor.
            </summary>
            <param name="env">The host environment.</param>
            <param name="weights">The array of weights vectors. It should contain <paramref name="numClasses"/> weights.</param>
            <param name="bias">The array of biases. It should contain contain <paramref name="numClasses"/> weights.</param>
            <param name="numClasses">The number of classes for multi-class classification. Must be at least 2.</param>
            <param name="numFeatures">The length of the feature vector.</param>
            <param name="labelNames">The optional label names. If specified not null, it should have the same length as <paramref name="numClasses"/>.</param>
            <param name="stats">The model statistics.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.Microsoft#ML#Model#ICanSaveInTextFormat#SaveAsText(System.IO.TextWriter,Microsoft.ML.Data.RoleMappedSchema)">
            <summary>
            Output the text model to a given writer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.Microsoft#ML#Model#ICanGetSummaryInKeyValuePairs#GetSummaryInKeyValuePairs(Microsoft.ML.Data.RoleMappedSchema)">
            <inheritdoc/>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.Microsoft#ML#Model#ICanSaveInSourceCode#SaveAsCode(System.IO.TextWriter,Microsoft.ML.Data.RoleMappedSchema)">
            <summary>
            Output the text model to a given writer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.GetWeights(Microsoft.ML.Data.VBuffer{System.Single}[]@,System.Int32@)">
            <summary>
            Copies the weight vector for each class into a set of buffers.
            </summary>
            <param name="weights">A possibly reusable set of vectors, which will
            be expanded as necessary to accomodate the data.</param>
            <param name="numClasses">Set to the rank, which is also the logical length
            of <paramref name="weights"/>.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters.GetBiases">
            <summary>
            Gets the biases for the logistic regression predictor.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.CoefficientStatistics">
            <summary>
            Represents a coefficient statistics object.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.LinearModelStatistics">
            <summary>
            The statistics for linear predictor.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearModelStatistics.TryGetBiasStatistics(Microsoft.ML.Trainers.LinearModelStatistics,System.Single,System.Single@,System.Single@,System.Single@)">
            <summary>
            Computes the standart deviation, Z-Score and p-Value.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearModelStatistics.GetCoefficientStatistics(Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.Data.DataView.DataViewSchema.Column,System.Int32)">
            <summary>
            Gets the coefficient statistics as an object.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearModelStatistics.SaveSummaryInKeyValuePairs(Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.Data.DataView.DataViewSchema.Column,System.Int32,System.Collections.Generic.List{System.Collections.Generic.KeyValuePair{System.String,System.Object}})">
            <summary>
            Support method for linear models and <see cref="T:Microsoft.ML.Model.ICanGetSummaryInKeyValuePairs"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.LabelColumn">
            <summary>
            The label column that the trainer expects.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.MetaMulticlassTrainer{`0,`1}.OptionsBase,System.String,System.String,Microsoft.ML.Trainers.ITrainerEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.IPredictorProducing{System.Single}},Microsoft.ML.IPredictorProducing{System.Single}},Microsoft.ML.Calibrators.ICalibratorTrainer)">
            <summary>
            Initializes the <see cref="T:Microsoft.ML.Trainers.MetaMulticlassTrainer`2"/> from the <see cref="T:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.OptionsBase"/> class.
            </summary>
            <param name="env">The private instance of the <see cref="T:Microsoft.ML.IHostEnvironment"/>.</param>
            <param name="options">The legacy arguments <see cref="T:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.OptionsBase"/>class.</param>
            <param name="name">The component name.</param>
            <param name="labelColumn">The label column for the metalinear trainer and the binary trainer.</param>
            <param name="singleEstimator">The binary estimator.</param>
            <param name="calibrator">The calibrator. If a calibrator is not explicitly provided, it will default to <see cref="T:Microsoft.ML.Calibrators.PlattCalibratorTrainer"/></param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.Microsoft#ML#ITrainer{Microsoft#ML#IPredictor}#Train(Microsoft.ML.TrainContext)">
            <summary>
            The legacy train method.
            </summary>
            <param name="context">The trainig context for this learner.</param>
            <returns>The trained model.</returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.GetOutputSchema(Microsoft.ML.SchemaShape)">
            <summary>
             Gets the output columns.
            </summary>
            <param name="inputSchema">The input schema. </param>
            <returns>The output <see cref="T:Microsoft.ML.SchemaShape"/></returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.MetadataForScoreColumn">
            <summary>
            Normal metadata that we produce for score columns.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MetaMulticlassTrainer`2.Fit(Microsoft.Data.DataView.IDataView)">
            <summary>
            Fits the data to the trainer.
            </summary>
            <param name="input">The input data to fit to.</param>
            <returns>The transformer.</returns>
        </member>
        <member name="P:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer.PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer.Info">
            <summary>
            Auxiliary information about the trainer in terms of its capabilities
            and requirements.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The name of the label column.</param>
            <param name="featureColumn">The name of the feature column.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer"/>
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.MultiClassNaiveBayesModelParameters.PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.MultiClassNaiveBayesModelParameters.GetLabelHistogram(System.Int32[]@,System.Int32@)">
            <summary>
            Copies the label histogram into a buffer.
            </summary>
            <param name="labelHistogram">A possibly reusable array, which will
            be expanded as necessary to accomodate the data.</param>
            <param name="labelCount">Set to the length of the resized array, which is also the number of different labels.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MultiClassNaiveBayesModelParameters.GetFeatureHistogram(System.Int32[][]@,System.Int32@,System.Int32@)">
            <summary>
            Copies the feature histogram into a buffer.
            </summary>
            <param name="featureHistogram">A possibly reusable array, which will
            be expanded as necessary to accomodate the data.</param>
            <param name="labelCount">Set to the first dimension of the resized array,
            which is the number of different labels encountered in training.</param>
            <param name="featureCount">Set to the second dimension of the resized array,
            which is also the number of different feature combinations encountered in training.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.MultiClassNaiveBayesModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Int32[],System.Int32[][],System.Int32)">
            <summary>
            Instantiates new model parameters from trained model.
            </summary>
            <param name="env">The host environment.</param>
            <param name="labelHistogram">The histogram of labels.</param>
            <param name="featureHistogram">The feature histogram.</param>
            <param name="featureCount">The number of features.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.Ova.Options">
            <summary>
            Options passed to OVA.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.Ova.Options.UseProbabilities">
            <summary>
            Whether to use probabilities (vs. raw outputs) to identify top-score category.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.Ova.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.Ova.Options)">
            <summary>
            Legacy constructor that builds the <see cref="T:Microsoft.ML.Trainers.Ova"/> trainer supplying the base trainer to use, for the classification task
            through the <see cref="T:Microsoft.ML.Trainers.Ova.Options"/>.
            Developers should instantiate OVA by supplying the trainer argument directly to the OVA constructor
            using the other public constructor.
            </summary>
            <param name="env">The private <see cref="T:Microsoft.ML.IHostEnvironment"/> for this estimator.</param>
            <param name="options">The legacy <see cref="T:Microsoft.ML.Trainers.Ova.Options"/></param>
        </member>
        <member name="M:Microsoft.ML.Trainers.Ova.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.ITrainerEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.IPredictorProducing{System.Single}},Microsoft.ML.IPredictorProducing{System.Single}},System.String,System.Boolean,Microsoft.ML.Calibrators.ICalibratorTrainer,System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.Ova"/>.
            </summary>
            <param name="env">The <see cref="T:Microsoft.ML.IHostEnvironment"/> instance.</param>
            <param name="binaryEstimator">An instance of a binary <see cref="T:Microsoft.ML.Trainers.ITrainerEstimator`2"/> used as the base trainer.</param>
            <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="T:Microsoft.ML.Calibrators.PlattCalibratorTrainer"/></param>
            <param name="labelColumn">The name of the label colum.</param>
            <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
            <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
            <param name="useProbabilities">Use probabilities (vs. raw outputs) to identify top-score category.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.Ova.Fit(Microsoft.Data.DataView.IDataView)">
            <summary> Trains and returns a <see cref="T:Microsoft.ML.Data.MulticlassPredictionTransformer`1"/>.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.OvaModelParameters.PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.OvaModelParameters.OutputFormula">
            <summary>
            Function applied to output of predictors. Assume that we have n predictors (one per class) and for the i-th predictor,
            y_i is its raw output and p_i is its probability output. Note that not all predictors are able to produce probability output.
            <para>
            <see cref="F:Microsoft.ML.Trainers.OvaModelParameters.OutputFormula.Raw"/>: output the result of predictors without post-processing. Output is [y_1, ..., y_n].
            <see cref="F:Microsoft.ML.Trainers.OvaModelParameters.OutputFormula.ProbabilityNormalization"/>: fetch probability output of each class probability from provided predictors and make sure the sume of class probabilities is one.
            Output is [p_1 / (p_1 + ... + p_n), ..., p_n / (p_1 + ... + p_n)].
            <see cref="F:Microsoft.ML.Trainers.OvaModelParameters.OutputFormula.Softmax"/>: Generate probability by feeding raw outputs to softmax function. Output is [z_1, ..., z_n], where z_i is exp(y_i) / (exp(y_1) + ... + exp(y_n)).
            </para>
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OvaModelParameters.Create(Microsoft.ML.IHost,Microsoft.ML.IPredictorProducing{System.Single}[])">
            <summary>
            Create a OVA predictor from an array of predictors.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OvaModelParameters.ImplDist.GetMapper">
            <summary>
            Each predictor produces a probability of a class. All classes' probabilities are normalized so that
            their sum is one.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.Pkpd">
             <summary>
             In this strategy, a binary classification algorithm is trained on each pair of classes.
             The pairs are unordered but created with replacement: so, if there were three classes, 0, 1,
             2, we would train classifiers for the pairs (0,0), (0,1), (0,2), (1,1), (1,2),
             and(2,2). For each binary classifier, an input data point is considered a
             positive example if it is in either of the two classes in the pair, and a
             negative example otherwise. At prediction time, the probabilities for each
             pair of classes is considered as the probability of being in either class of
             the pair given the data, and the final predictive probabilities out of that
             per class are calculated given the probability that an example is in any given
             pair.
            
             These two can allow you to exploit trainers that do not naturally have a
             multiclass option, for example, using the Runtime.FastTree.FastTreeBinaryClassificationTrainer
             to solve a multiclass problem.
             Alternately, it can allow ML.NET to solve a "simpler" problem even in the cases
             where the trainer has a multiclass option, but using it directly is not
             practical due to, usually, memory constraints.For example, while a multiclass
             logistic regression is a more principled way to solve a multiclass problem, it
             requires that the learner store a lot more intermediate state in the form of
             L-BFGS history for all classes *simultaneously*, rather than just one-by-one
             as would be needed for OVA.
             </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.Pkpd.Options">
            <summary>
            Options passed to PKPD.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.Pkpd.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.Pkpd.Options)">
            <summary>
            Legacy constructor that builds the <see cref="T:Microsoft.ML.Trainers.Pkpd"/> trainer supplying the base trainer to use, for the classification task
            through the <see cref="T:Microsoft.ML.Trainers.Pkpd.Options"/>Options.
            Developers should instantiate <see cref="T:Microsoft.ML.Trainers.Pkpd"/> by supplying the trainer argument directly to the <see cref="T:Microsoft.ML.Trainers.Pkpd"/> constructor
            using the other public constructor.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.Pkpd.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.ITrainerEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.IPredictorProducing{System.Single}},Microsoft.ML.IPredictorProducing{System.Single}},System.String,System.Boolean,Microsoft.ML.Calibrators.ICalibratorTrainer,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:Microsoft.ML.Trainers.Pkpd"/>
            </summary>
            <param name="env">The <see cref="T:Microsoft.ML.IHostEnvironment"/> instance.</param>
            <param name="binaryEstimator">An instance of a binary <see cref="T:Microsoft.ML.Trainers.ITrainerEstimator`2"/> used as the base trainer.</param>
            <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="T:Microsoft.ML.Calibrators.PlattCalibratorTrainer"/></param>
            <param name="labelColumn">The name of the label colum.</param>
            <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
            <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.Pkpd.Fit(Microsoft.Data.DataView.IDataView)">
            <summary>
            Fits the data to the transformer
            </summary>
            <param name="input">The input data.</param>
            <returns>The trained predictor.</returns>
        </member>
        <member name="P:Microsoft.ML.Trainers.PkpdModelParameters.PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.AveragedLinearOptions">
            <summary>
            Arguments class for averaged linear trainers.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.LearningRate">
            <summary>
            <a href="tmpurl_lr">Learning rate</a>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.DecreaseLearningRate">
            <summary>
            Determine whether to decrease the <see cref="F:Microsoft.ML.Trainers.AveragedLinearOptions.LearningRate"/> or not.
            </summary>
            <value>
            <see langword="true" /> to decrease the <see cref="F:Microsoft.ML.Trainers.AveragedLinearOptions.LearningRate"/> as iterations progress; otherwise, <see langword="false" />.
            Default is <see langword="false" />. The learning rate will be reduced with every weight update proportional to the square root of the number of updates.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.ResetWeightsAfterXExamples">
            <summary>
            Number of examples after which weights will be reset to the current average.
            </summary>
            <value>
            Default is <see langword="null" />, which disables this feature.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.DoLazyUpdates">
            <summary>
            Determines when to update averaged weights.
            </summary>
            <value>
            <see langword="true" /> to update averaged weights only when loss is nonzero.
            <see langword="false" /> to update averaged weights on every example.
            Default is <see langword="true" />.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.L2RegularizerWeight">
            <summary>
            The L2 weight for <a href='tmpurl_regularization'>regularization</a>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.RecencyGain">
            <summary>
            Extra weight given to more recent updates.
            </summary>
            <value>
            Default is 0, i.e. no extra gain.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.RecencyGainMulti">
            <summary>
            Determines whether <see cref="F:Microsoft.ML.Trainers.AveragedLinearOptions.RecencyGain"/> is multiplicative or additive.
            </summary>
            <value>
            <see langword="true" /> means <see cref="F:Microsoft.ML.Trainers.AveragedLinearOptions.RecencyGain"/> is multiplicative.
            <see langword="false" /> means <see cref="F:Microsoft.ML.Trainers.AveragedLinearOptions.RecencyGain"/> is additive.
            Default is <see langword="false" />.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.Averaged">
            <summary>
            Determines whether to do averaging or not.
            </summary>
            <value>
            <see langword="true" /> to do averaging; otherwise, <see langword="false" />.
            Default is <see langword="true" />.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedLinearOptions.AveragedTolerance">
            <summary>
            The inexactness tolerance for averaging.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.AveragedLinearTrainer`2.AveragedTrainStateBase.AveragedMargin(Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Return the raw margin from the decision hyperplane
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.AveragedLinearTrainer`2.AveragedTrainStateBase.IncrementAverageNonLazy">
            <summary>
            Add current weights and bias to average weights/bias.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.AveragedPerceptronTrainer">
             <summary>
             The <see cref="T:Microsoft.ML.IEstimator`1"/> for the averaged perceptron trainer.
             </summary>
             <remarks>
             The perceptron is a classification algorithm that makes its predictions by finding a separating hyperplane.
             For instance, with feature values f0, f1,..., f_D-1, the prediction is given by determining what side of the hyperplane the point falls into.
             That is the same as the sign of sigma[0, D-1] (w_i * f_i), where w_0, w_1,..., w_D-1 are the weights computed by the algorithm.
            
             The perceptron is an online algorithm, which means it processes the instances in the training set one at a time.
             It starts with a set of initial weights (zero, random, or initialized from a previous learner). Then, for each example in the training set, the weighted sum of the features (sigma[0, D-1] (w_i * f_i)) is computed.
             If this value has the same sign as the label of the current example, the weights remain the same. If they have opposite signs,
             the weights vector is updated by either adding or subtracting (if the label is positive or negative, respectively) the feature vector of the current example,
             multiplied by a factor 0 &lt; a &lt;= 1, called the learning rate. In a generalization of this algorithm, the weights are updated by adding the feature vector multiplied by the learning rate,
             and by the gradient of some loss function (in the specific case described above, the loss is hinge-loss, whose gradient is 1 when it is non-zero).
            
             In Averaged Perceptron (aka voted-perceptron), for each iteration, i.e. pass through the training data, a weight vector is calculated as explained above.
             The final prediction is then calculate by averaging the weighted sum from each weight vector and looking at the sign of the result.
            
             For more information see <a href="https://en.wikipedia.org/wiki/Perceptron">Wikipedia entry for Perceptron</a>
             or <a href="https://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.48.8200">Large Margin Classification Using the Perceptron Algorithm</a>
             </remarks>
        </member>
        <member name="T:Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options">
            <summary>
            Options for the <see cref="T:Microsoft.ML.Trainers.AveragedPerceptronTrainer"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options.LossFunction">
            <summary>
            A custom <a href="tmpurl_loss">loss</a>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options.Calibrator">
            <summary>
            The <a href="tmpurl_calib">calibrator</a> for producing probabilities. Default is exponential (aka Platt) calibration.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options.MaxCalibrationExamples">
            <summary>
            The maximum number of examples to use when training the calibrator.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.AveragedPerceptronTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,Microsoft.ML.IClassificationLoss,System.Single,System.Boolean,System.Single,System.Int32)">
            <summary>
            Trains a linear binary classifier using the averaged perceptron.
            <a href='https://en.wikipedia.org/wiki/Perceptron'>Wikipedia entry for Perceptron</a>
            </summary>
            <param name="env">The local instance of the <see cref="T:Microsoft.ML.IHostEnvironment"/></param>
            <param name="lossFunction">The classification loss function. </param>
            <param name="labelColumn">The name of the label column. </param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="learningRate">The learning rate. </param>
            <param name="decreaseLearningRate">Whether to decrease learning rate as iterations progress.</param>
            <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
            <param name="numIterations">The number of training iterations.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.LinearSvmTrainer">
            <summary>
            Linear SVM that implements PEGASOS for training. See: http://ttic.uchicago.edu/~shai/papers/ShalevSiSr07.pdf
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.LinearSvmTrainer.Options.WeightColumn">
            <summary>
            Column to use for example weight.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearSvmTrainer.TrainState.ProcessDataInstance(Microsoft.ML.IChannel,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,System.Single)">
            <summary>
            Observe an example and update weights if necesary.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearSvmTrainer.TrainState.UpdateWeights(Microsoft.ML.Data.VBuffer{System.Single}@,System.Single)">
            <summary>
            Updates the weights at the end of the batch. Since weightsUpdate can be an instance
            feature vector, this function should not change the contents of weightsUpdate.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearSvmTrainer.TrainState.Margin(Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Return the raw margin from the decision hyperplane.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearSvmTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,System.Int32)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.LinearSvmTrainer"/>.
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The name of the label column. </param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="weightColumn">The optional name of the weight column.</param>
            <param name="numIterations">The number of training iteraitons.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.OnlineGradientDescentTrainer">
            <summary>
        Stochastic gradient descent is an optimization method used to train a wide range of models in machine learning. 
        In the ML.Net the implementation of OGD, is for linear regression. 
      </summary><remarks>
        Stochastic gradient descent uses a simple yet efficient iterative technique to fit model coefficients using error gradients for convex loss functions.
        The OnlineGradientDescentRegressor implements the standard (non-batch) SGD, with a choice of loss functions,
        and an option to update the weight vector using the average of the vectors seen over time (averaged argument is set to True by default).
      </remarks>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineGradientDescentTrainer.Options.#ctor">
            <summary>
            Set defaults that vary from the base type.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineGradientDescentTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.Single,System.Boolean,System.Single,System.Int32,Microsoft.ML.IRegressionLoss)">
            <summary>
            Trains a new <see cref="T:Microsoft.ML.Data.RegressionPredictionTransformer`1"/>.
            </summary>
            <param name="env">The pricate instance of <see cref="T:Microsoft.ML.IHostEnvironment"/>.</param>
            <param name="labelColumn">Name of the label column.</param>
            <param name="featureColumn">Name of the feature column.</param>
            <param name="learningRate">The learning Rate.</param>
            <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
            <param name="l2RegularizerWeight">L2 Regularization Weight.</param>
            <param name="numIterations">Number of training iterations through the data.</param>
            <param name="lossFunction">The custom loss functions. Defaults to <see cref="T:Microsoft.ML.SquaredLoss"/> if not provided.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.OnlineLinearOptions">
            <summary>
            Arguments class for online linear trainers.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearOptions.NumberOfIterations">
            <summary>
            Number of passes through the training dataset.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearOptions.InitialWeights">
            <summary>
            Initial weights and bias, comma-separated.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearOptions.InitialWeightsDiameter">
            <summary>
            Initial weights and bias scale.
            </summary>
            <value>
            This property is only used if the provided value is positive and <see cref="F:Microsoft.ML.Trainers.OnlineLinearOptions.InitialWeights"/> is not specified.
            The weights and bias will be randomly selected from InitialWeights * [-0.5,0.5] interval with uniform distribution.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearOptions.Shuffle">
            <summary>
            Determines whether to shuffle data for each training iteration.
            </summary>
            <value>
            <see langword="true" /> to shuffle data for each training iteration; otherwise, <see langword="false" />.
            Default is <see langword="true" />.
            </value>
        </member>
        <member name="T:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase">
            <summary>
            An object to hold the mutable updatable state for the online linear trainers. Specific algorithms should subclass
            this, and return the instance via <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.MakeState(Microsoft.ML.IChannel,System.Int32,Microsoft.ML.Trainers.LinearModelParameters)"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Iteration">
            <summary>
            The number of iterations. Incremented by <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.BeginIteration(Microsoft.ML.IChannel)"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.NumIterExamples">
            <summary>
            The number of examples in the current iteration. Incremented by <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.ProcessDataInstance(Microsoft.ML.IChannel,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,System.Single)"/>,
            and reset by <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.BeginIteration(Microsoft.ML.IChannel)"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Weights">
            <summary>
            Current weights. The weights vector is considered to be scaled by <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.WeightsScale"/>. Storing this separately
            allows us to avoid the overhead of an explicit scaling, which some algorithms will attempt to do on each example's update.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.WeightsScale">
            <summary>
            The implicit scaling factor for <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Weights"/>. Note that this does not affect <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Bias"/>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Bias">
            <summary>
            The intercept term.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.ScaleWeights">
            <summary>
            Propagates the <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.WeightsScale"/> to the <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Weights"/> vector.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.ScaleWeightsIfNeeded">
            <summary>
            Conditionally propagates the <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.WeightsScale"/> to the <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Weights"/> vector
            when it reaches a scale where additions to weights would start dropping too much precision.
            ("Too much" is mostly empirically defined.)
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.BeginIteration(Microsoft.ML.IChannel)">
            <summary>
            Called by <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainCore(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,Microsoft.ML.Trainers.OnlineLinearTrainer{`0,`1}.TrainStateBase)"/> at the start of a pass over the dataset.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.FinishIteration(Microsoft.ML.IChannel)">
            <summary>
            Called by <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainCore(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,Microsoft.ML.Trainers.OnlineLinearTrainer{`0,`1}.TrainStateBase)"/> after a pass over the dataset.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.ProcessDataInstance(Microsoft.ML.IChannel,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,System.Single)">
            <summary>
            This should be overridden by derived classes. This implementation simply increments <see cref="F:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.NumIterExamples"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.CurrentMargin(Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Return the raw margin from the decision hyperplane
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.Margin(Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            The default implementation just calls <see cref="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.TrainStateBase.CurrentMargin(Microsoft.ML.Data.VBuffer{System.Single}@)"/>.
            </summary>
            <param name="feat"></param>
            <returns></returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.OnlineLinearTrainer`2.Fit(Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.LinearModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.OnlineLinearTrainer`2"/> using an already trained <paramref name="modelParameters"/> and returns a <see cref="T:Microsoft.ML.ITransformer"/>.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.PoissonRegression">
            <summary>
        Trains a Poisson Regression model.  
      </summary><remarks>
        <a href="https://en.wikipedia.org/wiki/Poisson_regression">Poisson regression</a> is a parameterized regression method.
        It assumes that the log of the conditional mean of the dependent variable follows a linear function of the dependent variables.
        Assuming that the dependent variable follows a Poisson distribution, the parameters of the regressor can be estimated by maximizing the likelihood of the obtained observations.
      </remarks>
        </member>
        <member name="M:Microsoft.ML.Trainers.PoissonRegression.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The name of the label column.</param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="weights">The name for the example weight column.</param>
            <param name="l1Weight">Weight of L1 regularizer term.</param>
            <param name="l2Weight">Weight of L2 regularizer term.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.PoissonRegression.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.PoissonRegression.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/>
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.PoissonRegression.Fit(Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.LinearModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/> using an already trained <paramref name="linearModel"/> and returns
            a <see cref="T:Microsoft.ML.Data.RegressionPredictionTransformer`1"/>.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.LinearTrainerBase`2.ShuffleData">
            <summary>
            Whether data is to be shuffled every epoch.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LinearTrainerBase`2.PrepareDataFromTrainingExamples(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,System.Int32@)">
            <summary>
            This method ensures that the data meets the requirements of this trainer and its
            subclasses, injects necessary transforms, and throws if it couldn't meet them.
            </summary>
            <param name="ch">The channel</param>
            <param name="examples">The training examples</param>
            <param name="weightSetCount">Gets the length of weights and bias array. For binary classification and regression,
            this is 1. For multi-class classification, this equals the number of classes on the label.</param>
            <returns>A potentially modified version of <paramref name="examples"/></returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.InitializeConvergenceMetrics(System.String[]@,System.Double[]@)">
            <summary>
            Returns the names of the metrics reported by <see cref="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.CheckConvergence(Microsoft.ML.IProgressChannel,System.Int32,Microsoft.ML.Trainers.FloatLabelCursor.Factory,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.DualsTableBase,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.IdToIdxLookup,Microsoft.ML.Data.VBuffer{System.Single}[],Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[],System.Single[],System.Single[],System.Int64,System.Double[],System.Double@,System.Int32@)"/>, as well as the initial values.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.TrainWithoutLock(Microsoft.ML.IProgressChannelProvider,Microsoft.ML.Trainers.FloatLabelCursor.Factory,System.Random,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.IdToIdxLookup,System.Int32,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.DualsTableBase,System.Single[],System.Single[],System.Single,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[])">
            <summary>
            Train the SDCA optimizer with one iteration over the entire training examples.
            </summary>
            <param name="progress">The progress reporting channel.</param>
            <param name="cursorFactory">The cursor factory to create cursors over the training examples.</param>
            <param name="rand">
            The random number generator to generate random numbers for randomized shuffling of the training examples.
            It may be null. When it is null, the training examples are not shuffled and are cursored in its original order.
            </param>
            <param name="idToIdx">
            The id to index mapping. May be null. If it is null, the index is given by the
            corresponding lower bits of the id.
            </param>
            <param name="numThreads">The number of threads used in parallel training. It is used in computing the dual update.</param>
            <param name="duals">
            The dual variables. For binary classification and regression, there is one dual variable per row.
            For multiclass classification, there is one dual variable per class per row.
            </param>
            <param name="biasReg">The array containing regularized bias terms. For binary classification or regression,
            it contains only a single value. For multiclass classification its size equals the number of classes.</param>
            <param name="invariants">
            The dual updates invariants. It may be null. If not null, it holds an array of pre-computed numerical quantities
            that depend on the training example label and features, not the value of dual variables.
            </param>
            <param name="lambdaNInv">The precomputed numerical quantity 1 / (l2Const * (count of training examples)).</param>
            <param name="weights">
            The weights array. For binary classification or regression, it consists of only one VBuffer.
            For multiclass classification, its size equals the number of classes.
            </param>
            <param name="biasUnreg">
            The array containing unregularized bias terms. For binary classification or regression,
            it contains only a single value. For multiclass classification its size equals the number of classes.
            </param>
            <param name="l1IntermediateWeights">
            The array holding the intermediate weights prior to making L1 shrinkage adjustment. It is null iff l1Threshold is zero.
            Otherwise, for binary classification or regression, it consists of only one VBuffer;
            for multiclass classification, its size equals the number of classes.
            </param>
            <param name="l1IntermediateBias">
            The array holding the intermediate bias prior to making L1 shrinkage adjustment. It is null iff l1Threshold is zero.
            Otherwise, for binary classification or regression, it consists of only one value;
            for multiclass classification, its size equals the number of classes.
            </param>
            <param name="featureNormSquared">
            The array holding the pre-computed squared L2-norm of features for each training example. It may be null. It is always null for
            binary classification and regression because this quantity is not needed.
            </param>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.CheckConvergence(Microsoft.ML.IProgressChannel,System.Int32,Microsoft.ML.Trainers.FloatLabelCursor.Factory,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.DualsTableBase,Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.IdToIdxLookup,Microsoft.ML.Data.VBuffer{System.Single}[],Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[],System.Single[],System.Single[],System.Int64,System.Double[],System.Double@,System.Int32@)">
            <summary>
             Returns whether the algorithm converged, and also populates the <paramref name="metrics"/>
            (which is expected to be parallel to the names returned by <see cref="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.InitializeConvergenceMetrics(System.String[]@,System.Double[]@)"/>).
            When called, the <paramref name="metrics"/> is expected to hold the previously reported values.
            </summary>
            <param name="pch">The progress reporting channel.</param>
            <param name="iter">The iteration number, zero based.</param>
            <param name="cursorFactory">The cursor factory to create cursors over the training data.</param>
            <param name="duals">
            The dual variables. For binary classification and regression, there is one dual variable per row.
            For multiclass classification, there is one dual variable per class per row.
            </param>
            <param name="idToIdx">
            The id to index mapping. May be null. If it is null, the index is given by the
            corresponding lower bits of the id.
            </param>
            <param name="weights">
            The weights array. For binary classification or regression, it consists of only one VBuffer.
            For multiclass classification, its size equals the number of classes.
            </param>
            <param name="bestWeights">
            The weights array that corresponds to the best model obtained from the training iterations thus far.
            </param>
            <param name="biasUnreg">
            The array containing unregularized bias terms. For binary classification or regression,
            it contains only a single value. For multiclass classification its size equals the number of classes.
            </param>
            <param name="bestBiasUnreg">
            The array containing unregularized bias terms corresponding to the best model obtained from the training iterations thus far.
            For binary classification or regression, it contains only a single value.
            For multiclass classification its size equals the number of classes.
            </param>
            <param name="biasReg">
            The array containing regularized bias terms. For binary classification or regression,
            it contains only a single value. For multiclass classification its size equals the number of classes.
            </param>
            <param name="bestBiasReg">
            The array containing regularized bias terms corresponding to the best model obtained from the training iterations thus far.
            For binary classification or regression, it contains only a single value.
            For multiclass classification its size equals the number of classes.
            </param>
            <param name="count">
            The count of (valid) training examples. Bad training examples are excluded from this count.
            </param>
            <param name="metrics">
            The array of metrics for progress reporting.
            </param>
            <param name="bestPrimalLoss">
            The primal loss function value corresponding to the best model obtained thus far.
            </param>
            <param name="bestIter">The iteration number when the best model is obtained.</param>
            <returns>Whether the optimization has converged.</returns>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.DualsTableBase">
            <summary>
            Encapsulates the common functionality of storing and
            retrieving the dual variables.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.StandardArrayDualsTable">
            <summary>
            Implementation of <see cref="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.DualsTableBase"/> using a standard array.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.BigArrayDualsTable">
            <summary>
            Implementation of <see cref="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.DualsTableBase"/> using a big array.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.GetIndexFromIdGetter(Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.IdToIdxLookup,System.Int32)">
            <summary>
            Returns a function delegate to retrieve index from id.
            This is to avoid redundant conditional branches in the tight loop of training.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.GetIndexFromIdAndRowGetter(Microsoft.ML.Trainers.SdcaTrainerBase{`0,`1,`2}.IdToIdxLookup,System.Int32)">
            <summary>
            Returns a function delegate to retrieve index from id and row.
            Only works if the cursor is not shuffled.
            This is to avoid redundant conditional branches in the tight loop of training.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup">
            <summary>
            A hash table data structure to store Id of type <see cref="T:Microsoft.Data.DataView.DataViewRowId"/>,
            and accommodates size larger than 2 billion. This class is an extension based on BCL.
            Two operations are supported: adding and retrieving an id with asymptotically constant complexity.
            The bucket size are prime numbers, starting from 3 and grows to the next prime larger than
            double the current size until it reaches the maximum possible size. When a table growth is triggered,
            the table growing operation initializes a new larger bucket and rehash the existing entries to
            the new bucket. Such operation has an expected complexity proportional to the size.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.Count">
            <summary>
            Gets the count of id entries.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.#ctor(System.Int64)">
            <summary>
            Initializes an instance of the <see cref="T:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup"/> class with the specified size.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.Add(Microsoft.Data.DataView.DataViewRowId)">
            <summary>
            Make sure the given id is in this lookup table and return the index of the id.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.TryGetIndex(Microsoft.Data.DataView.DataViewRowId,System.Int64@)">
            <summary>
            Find the index of the given id.
            Returns a bool representing if id is present.
            Index outputs the index that the id, -1 otherwise.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.GetIndexCore(Microsoft.Data.DataView.DataViewRowId,System.Int64)">
            <summary>
            Return the index of value, -1 if it is not present.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaTrainerBase`3.IdToIdxLookup.AddCore(Microsoft.Data.DataView.DataViewRowId,System.Int64)">
            <summary>
            Adds the value as a TItem. Does not check whether the TItem is already present.
            Returns the index of the added value.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.CompensatedSum">
            <summary>
            Sum with underflow compensation for better numerical stability.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaBinaryTrainerBase`1">
            <summary>
            SDCA is a general training algorithm for (generalized) linear models such as support vector machine, linear regression, logistic regression,
            and so on. SDCA binary classification trainer family includes several sealed members:
            (1) <see cref="T:Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer"/> supports general loss functions and returns <see cref="T:Microsoft.ML.Trainers.LinearBinaryModelParameters"/>.
            (2) <see cref="T:Microsoft.ML.Trainers.SdcaBinaryTrainer"/> essentially trains a regularized logistic regression model. Because logistic regression
            naturally provide probability output, this generated model's type is <see cref="T:Microsoft.ML.Calibrators.CalibratedModelParametersBase`2"/>.
            where <see langword="TSubModel"/> is <see cref="T:Microsoft.ML.Trainers.LinearBinaryModelParameters"/> and <see langword="TCalibrator "/> is <see cref="T:Microsoft.ML.Calibrators.PlattCalibrator"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaBinaryTrainerBase`1.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaClassificationLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SdcaBinaryTrainerBase`1"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumnName">The label, or dependent variable.</param>
            <param name="featureColumnName">The features, or independent variables.</param>
            <param name="loss">The custom loss.</param>
            <param name="weightColumnName">The optional example weights.</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaBinaryTrainer.Options">
            <summary>
            Configuration to training logistic regression using SDCA.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options">
            <summary>
            General Configuration to training linear model using SDCA.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.CreatePredictor(Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[])">
            <summary>
            Comparing with <see cref="M:Microsoft.ML.Trainers.SdcaBinaryTrainer.CreatePredictor(Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[])"/>,
            <see cref="M:Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.CreatePredictor(Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[])"/> directly outputs a <see cref="T:Microsoft.ML.Trainers.LinearBinaryModelParameters"/> built from
            the learned weights and bias without calibration.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.LegacySdcaBinaryTrainer">
            <summary>
            <see cref="T:Microsoft.ML.Trainers.LegacySdcaBinaryTrainer"/> is used to support classical command line tools where model is weakly-typed to
            <see cref="T:Microsoft.ML.Model.IPredictorWithFeatureWeights`1"/>. Please do NOT use it whenever possible.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.LegacySdcaBinaryTrainer.Options">
            <summary>
            Legacy configuration to SDCA in legacy framework.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LegacySdcaBinaryTrainer.CreatePredictor(Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[])">
            <summary>
            Weekly-typed function to create calibrated or uncalibrated predictors.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.L2Weight">
            <summary>
            The L2 weight for <a href='tmpurl_regularization'>regularization</a>.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.NumThreads">
            <summary>
            The degree of lock-free parallelism used by SGD.
            </summary>
            <value>
            Defaults to automatic depending on data sparseness. Determinism is not guaranteed.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.ConvergenceTolerance">
            <summary>
            The convergence tolerance. If the exponential moving average of loss reductions falls below this tolerance,
            the algorithm is deemed to have converged and will stop.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.MaxIterations">
            <summary>
            The maximum number of passes through the training dataset.
            </summary>
            <value>
            Set to 1 to simulate online learning.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.InitLearningRate">
            <summary>
            The initial <a href="tmpurl_lr">learning rate</a> used by SGD.
            </summary>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.Shuffle">
            <summary>
            Determines whether to shuffle data for each training iteration.
            </summary>
            <value>
            <see langword="true" /> to shuffle data for each training iteration; otherwise, <see langword="false" />.
            Default is <see langword="true" />.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.PositiveInstanceWeight">
            <summary>
            The weight to be applied to the positive class. This is useful for training with imbalanced data.
            </summary>
            <value>
            Default value is 1, which means no extra weight.
            </value>
        </member>
        <member name="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.CheckFrequency">
            <summary>
            Determines the frequency of checking for convergence in terms of number of iterations.
            </summary>
            <value>
            Default equals <see cref="F:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.OptionsBase.NumThreads"/>."
            </value>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,Microsoft.ML.IClassificationLoss,System.Int32,System.Double,System.Single)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="featureColumn">The name of the feature column.</param>
            <param name="labelColumn">The name of the label column.</param>
            <param name="weightColumn">The name of the example weight column.</param>
            <param name="maxIterations">The maximum number of iterations; set to 1 to simulate online learning.</param>
            <param name="initLearningRate">The initial learning rate used by SGD.</param>
            <param name="l2Weight">The L2 regularizer constant.</param>
            <param name="loss">The loss function to use.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.SgdBinaryTrainerBase{`0}.OptionsBase,Microsoft.ML.IClassificationLoss,System.Boolean)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
            <param name="loss">Loss function would be minimized.</param>
            <param name="doCalibration">Set to true if a calibration step should be happen after training. Use false otherwise.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.Fit(Microsoft.Data.DataView.IDataView,Microsoft.ML.Trainers.LinearModelParameters)">
            <summary>
            Continues the training of a <see cref="T:Microsoft.ML.Trainers.SdcaBinaryTrainer"/> using an already trained <paramref name="modelParameters"/> and returns a <see cref="T:Microsoft.ML.Data.BinaryPredictionTransformer"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)">
            <summary>
            <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)"/> implements a mechanism to craft a typed model out from linear weights and a bias.
            It's used at the end of <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.TrainCore(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,Microsoft.ML.Trainers.LinearModelParameters,System.Int32)"/> to finalize the trained model.
            Derived classes should implement <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)"/> because different trainers may produce different
            types of models.
            </summary>
            <param name="weights">Weights of linear model.</param>
            <param name="bias">Bias of linear model.</param>
            <returns>A model built upon weights and bias. It can be as simple as a <see cref="T:Microsoft.ML.Trainers.LinearBinaryModelParameters"/>.</returns>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.CreateLinearBinaryModelParameters(Microsoft.ML.Data.VBuffer{System.Single},System.Single)">
            <summary>
            A helper function used to create <see cref="T:Microsoft.ML.Trainers.LinearBinaryModelParameters"/> in implementations of <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)"/>.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SgdBinaryTrainer">
            <summary>
            The <see cref="T:Microsoft.ML.IEstimator`1"/> for training logistic regression using a parallel stochastic gradient method.
            The trained model is <a href='tmpurl_calib'>calibrated</a> and can produce probability by feeding the output value of the
            linear function to a <see cref="T:Microsoft.ML.Calibrators.PlattCalibrator"/>.
            </summary>
            <remarks>
            The Stochastic Gradient Descent (SGD) is one of the popular stochastic optimization procedures that can be integrated
            into several machine learning tasks to achieve state-of-the-art performance. This trainer implements the Hogwild SGD for binary classification
            that supports multi-threading without any locking. If the associated optimization problem is sparse, Hogwild SGD achieves a nearly optimal
            rate of convergence. For more details about Hogwild SGD, please refer to http://arxiv.org/pdf/1106.5730v2.pdf.
            </remarks>
        </member>
        <member name="T:Microsoft.ML.Trainers.SgdBinaryTrainer.Options">
            <summary>
            Options for the <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainer"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.SgdBinaryTrainer.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainer.GetOutputColumnsCore(Microsoft.ML.SchemaShape)">
            <summary>
            Logistic regression's output can naturally be interpreted as probability, so this model has three output columns.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdBinaryTrainer.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)">
            <summary>
            Given weights and bias trained in <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.TrainCore(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,Microsoft.ML.Trainers.LinearModelParameters,System.Int32)"/>,
            <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainer.CreateModel(Microsoft.ML.Data.VBuffer{System.Single},System.Single)"/> produces the final calibrated linear model.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer">
            <summary>
            <see cref="T:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer"/> can train a linear classification model by minimizing any loss function
            which implements <see cref="T:Microsoft.ML.IClassificationLoss"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.LegacySgdBinaryTrainer">
            <summary>
            <see cref="T:Microsoft.ML.Trainers.LegacySgdBinaryTrainer"/> is used to support classical command line
            tools where model is weakly-typed to <see cref="T:Microsoft.ML.Model.IPredictorWithFeatureWeights`1"/>. Please do NOT use it
            whenever possible.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.LegacySgdBinaryTrainer.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.Trainers.LegacySgdBinaryTrainer.Options)">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.LegacySgdBinaryTrainer.GetOutputColumnsCore(Microsoft.ML.SchemaShape)">
            <summary>
            <see cref="T:Microsoft.ML.LogLoss"/> leads to logistic regression which naturally supports probablity output. For other loss functions,
            a calibrator would be added after <see cref="M:Microsoft.ML.Trainers.SgdBinaryTrainerBase`1.TrainCore(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,Microsoft.ML.Trainers.LinearModelParameters,System.Int32)"/>
            finishing its job. Therefore, we always have three output columns in the legacy world.
            </summary>
            <param name="inputSchema"></param>
            <returns></returns>
        </member>
        <member name="T:Microsoft.ML.Trainers.Sdca">
            <summary>
            A component to train an SDCA model.
            </summary>
            <summary>
            The Entry Point for SDCA multiclass.
            </summary>
             <summary>
            The Entry Point for the SDCA regressor.
             </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaMultiClassTrainer">
            <summary>
        Train an SDCA linear model.
      </summary><remarks>
        This classifier is a trainer based on the Stochastic Dual Coordinate Ascent(SDCA) method, a state-of-the-art optimization technique for convex objective functions.
        The algorithm can be scaled for use on large out-of-memory data sets due to a semi-asynchronized implementation that supports multi-threading.
        <para>
          Convergence is underwritten by periodically enforcing synchronization between primal and dual updates in a separate thread.
          Several choices of loss functions are also provided.
          The SDCA method combines several of the best properties and capabilities of logistic regression and SVM algorithms.
        </para>
        <para>
          Note that SDCA is a stochastic and streaming optimization algorithm.
          The results depends on the order of the training data. For reproducible results, it is recommended that one sets 'Shuffle' to
          False and 'NumThreads' to 1.
          Elastic net regularization can be specified by the 'L2Const' and 'L1Threshold' parameters. Note that the 'L2Const' has an effect on the rate of convergence.
          In general, the larger the 'L2Const', the faster SDCA converges.
        </para>
        <para>For more information, see:</para>
        <list type="bullet">
          <item><description>
            <a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/main-3.pdf">Scaling Up Stochastic Dual Coordinate Ascent</a>.
          </description></item>
          <item><description>
            <a href="http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf">Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization</a>.
          </description></item>
        </list>
       </remarks>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaMultiClassTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaClassificationLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SdcaMultiClassTrainer"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The label, or dependent variable.</param>
            <param name="featureColumn">The features, or independent variables.</param>
            <param name="weights">The optional example weights.</param>
            <param name="loss">The custom loss.</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaMultiClassTrainer.TrainWithoutLock(Microsoft.ML.IProgressChannelProvider,Microsoft.ML.Trainers.FloatLabelCursor.Factory,System.Random,Microsoft.ML.Trainers.SdcaTrainerBase{Microsoft.ML.Trainers.SdcaMultiClassTrainer.Options,Microsoft.ML.Data.MulticlassPredictionTransformer{Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters},Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters}.IdToIdxLookup,System.Int32,Microsoft.ML.Trainers.SdcaTrainerBase{Microsoft.ML.Trainers.SdcaMultiClassTrainer.Options,Microsoft.ML.Data.MulticlassPredictionTransformer{Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters},Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters}.DualsTableBase,System.Single[],System.Single[],System.Single,Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[])">
            <inheritdoc/>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaMultiClassTrainer.CheckConvergence(Microsoft.ML.IProgressChannel,System.Int32,Microsoft.ML.Trainers.FloatLabelCursor.Factory,Microsoft.ML.Trainers.SdcaTrainerBase{Microsoft.ML.Trainers.SdcaMultiClassTrainer.Options,Microsoft.ML.Data.MulticlassPredictionTransformer{Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters},Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters}.DualsTableBase,Microsoft.ML.Trainers.SdcaTrainerBase{Microsoft.ML.Trainers.SdcaMultiClassTrainer.Options,Microsoft.ML.Data.MulticlassPredictionTransformer{Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters},Microsoft.ML.Trainers.MulticlassLogisticRegressionModelParameters}.IdToIdxLookup,Microsoft.ML.Data.VBuffer{System.Single}[],Microsoft.ML.Data.VBuffer{System.Single}[],System.Single[],System.Single[],System.Single[],System.Single[],System.Int64,System.Double[],System.Double@,System.Int32@)">
            <inheritdoc/>
        </member>
        <member name="T:Microsoft.ML.Trainers.SdcaRegressionTrainer">
            <summary>
        Train an SDCA linear model.
      </summary><remarks>
        This classifier is a trainer based on the Stochastic Dual Coordinate Ascent(SDCA) method, a state-of-the-art optimization technique for convex objective functions.
        The algorithm can be scaled for use on large out-of-memory data sets due to a semi-asynchronized implementation that supports multi-threading.
        <para>
          Convergence is underwritten by periodically enforcing synchronization between primal and dual updates in a separate thread.
          Several choices of loss functions are also provided.
          The SDCA method combines several of the best properties and capabilities of logistic regression and SVM algorithms.
        </para>
        <para>
          Note that SDCA is a stochastic and streaming optimization algorithm.
          The results depends on the order of the training data. For reproducible results, it is recommended that one sets 'Shuffle' to
          False and 'NumThreads' to 1.
          Elastic net regularization can be specified by the 'L2Const' and 'L1Threshold' parameters. Note that the 'L2Const' has an effect on the rate of convergence.
          In general, the larger the 'L2Const', the faster SDCA converges.
        </para>
        <para>For more information, see:</para>
        <list type="bullet">
          <item><description>
            <a href="https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/main-3.pdf">Scaling Up Stochastic Dual Coordinate Ascent</a>.
          </description></item>
          <item><description>
            <a href="http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf">Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization</a>.
          </description></item>
        </list>
       </remarks>
        </member>
        <member name="M:Microsoft.ML.Trainers.SdcaRegressionTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaRegressionLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Initializes a new instance of <see cref="T:Microsoft.ML.Trainers.SdcaRegressionTrainer"/>
            </summary>
            <param name="env">The environment to use.</param>
            <param name="labelColumn">The label, or dependent variable.</param>
            <param name="featureColumn">The features, or independent variables.</param>
            <param name="weights">The optional example weights.</param>
            <param name="loss">The custom loss.</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
        </member>
        <member name="T:Microsoft.ML.Trainers.RandomTrainer">
            <summary>
            A trainer that trains a predictor that returns random values
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.RandomTrainer.Microsoft#ML#ITrainer#PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.RandomTrainer.Info">
            <summary>
            Auxiliary information about the trainer in terms of its capabilities
            and requirements.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomTrainer.#ctor(Microsoft.ML.IHostEnvironment)">
            <summary>
            Initializes RandomTrainer object.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomTrainer.Fit(Microsoft.Data.DataView.IDataView)">
            <summary>
            Trains and returns a <see cref="T:Microsoft.ML.Data.BinaryPredictionTransformer`1"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomTrainer.GetOutputSchema(Microsoft.ML.SchemaShape)">
            <summary>
            Returns the <see cref="T:Microsoft.ML.SchemaShape"/> of the schema which will be produced by the transformer.
            Used for schema propagation and verification in a pipeline.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Trainers.RandomModelParameters">
            <summary>
            The predictor implements the Predict() interface. The predictor returns a
             uniform random probability and classification assignment.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.RandomModelParameters.PredictionKind">
            <summary>Return the type of prediction task.</summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Int32)">
            <summary>
            Instantiate a model that returns a uniform random probability.
            </summary>
            <param name="env">The host environment.</param>
            <param name="seed">The random seed.</param>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomModelParameters.#ctor(Microsoft.ML.IHostEnvironment,Microsoft.ML.ModelLoadContext)">
            <summary>
            Load the predictor from the binary format.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.RandomModelParameters.SaveCore(Microsoft.ML.ModelSaveContext)">
            <summary>
            Save the predictor in the binary format.
            </summary>
            <param name="ctx"></param>
        </member>
        <member name="T:Microsoft.ML.Trainers.PriorTrainer">
            <summary>
            Learns the prior distribution for 0/1 class labels and outputs that.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.PriorTrainer.Microsoft#ML#ITrainer#PredictionKind">
            <summary> Return the type of prediction task.</summary>
        </member>
        <member name="P:Microsoft.ML.Trainers.PriorTrainer.Info">
            <summary>
            Auxiliary information about the trainer in terms of its capabilities
            and requirements.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.PriorTrainer.#ctor(Microsoft.ML.IHostEnvironment,System.String,System.String)">
            <summary>
            Initializes PriorTrainer object.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.PriorTrainer.Fit(Microsoft.Data.DataView.IDataView)">
            <summary>
            Trains and returns a <see cref="T:Microsoft.ML.Data.BinaryPredictionTransformer`1"/>.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.PriorTrainer.GetOutputSchema(Microsoft.ML.SchemaShape)">
            <summary>
            Returns the <see cref="T:Microsoft.ML.SchemaShape"/> of the schema which will be produced by the transformer.
            Used for schema propagation and verification in a pipeline.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.PriorModelParameters.#ctor(Microsoft.ML.IHostEnvironment,System.Single)">
            <summary>
            Instantiates a model that returns the prior probability of the positive class in the training set.
            </summary>
            <param name="env">The host environment.</param>
            <param name="prob">The probability of the positive class.</param>
        </member>
        <member name="P:Microsoft.ML.Trainers.StochasticTrainerBase`2.ShuffleData">
            <summary>
            Whether data is to be shuffled every epoch.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Trainers.StochasticTrainerBase`2.PrepareDataFromTrainingExamples(Microsoft.ML.IChannel,Microsoft.ML.Data.RoleMappedData,System.Int32@)">
            <summary>
            This method ensures that the data meets the requirements of this trainer and its
            subclasses, injects necessary transforms, and throws if it couldn't meet them.
            </summary>
            <param name="ch">The channel</param>
            <param name="examples">The training examples</param>
            <param name="weightSetCount">Gets the length of weights and bias array. For binary classification and regression,
            this is 1. For multi-class classification, this equals the number of classes on the label.</param>
            <returns>A potentially modified version of <paramref name="examples"/></returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.DifferentiableFunction">
            <summary>
            A delegate for functions with gradients.
            </summary>
            <param name="input">The point at which to evaluate the function</param>
            <param name="gradient">The gradient vector, which must be filled in (its initial contents are undefined)</param>
            <param name="progress">The progress channel provider that can be used to report calculation progress. Can be null.</param>
            <returns>The value of the function</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.IndexedDifferentiableFunction">
             <summary>
             A delegate for indexed sets of functions with gradients.
            
             REVIEW: I didn't add an <see cref="T:Microsoft.ML.IProgressChannelProvider"/> here, since it looks like this code is not actually
             accessed from anywhere. Maybe it should go away?
             </summary>
             <param name="index">The index of the function</param>
             <param name="input">The point at which to evaluate the function</param>
             <param name="gradient">The gradient vector, which must be filled in (its initial contents are undefined)</param>
             <returns>The value of the function</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.DifferentiableFunctionAggregator">
            <summary>
            Class to aggregate an indexed differentiable function into a single function, in parallel
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.DifferentiableFunctionAggregator.#ctor(Microsoft.ML.Numeric.IndexedDifferentiableFunction,System.Int32,System.Int32,System.Int32)">
            <summary>
            Creates a DifferentiableFunctionAggregator
            </summary>
            <param name="func">Indexed function to use</param>
            <param name="dim">Dimensionality of the function</param>
            <param name="maxIndex">Max index of the function</param>
            <param name="threads">Number of threads to use</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.DifferentiableFunctionAggregator.Eval(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Evaluate and sum the function over all indices, in parallel
            </summary>
            <param name="input">The point at which to evaluate the function</param>
            <param name="gradient">The gradient vector, which must be filled in (its initial contents are undefined)</param>
            <returns>Function value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.GradientTester">
            <summary>
            A class for testing the gradient of DifferentiableFunctions, useful for debugging
            </summary>
            <remarks>
            Works by comparing the reported gradient to the numerically computed gradient.
            If the gradient is correct, the return value should be small (order of 1e-6).
            May have false negatives if extreme values cause the numeric gradient to be off,
            for example, if the norm of x is very large, or if the gradient is changing rapidly at x.
            </remarks>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientTester.Test(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Tests the gradient reported by f.
            </summary>
            <param name="f">function to test</param>
            <param name="x">point at which to test</param>
            <returns>maximum normalized difference between analytic and numeric directional derivative over multiple tests</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientTester.Test(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,System.Boolean)">
            <summary>
            Tests the gradient reported by f.
            </summary>
            <param name="f">function to test</param>
            <param name="x">point at which to test</param>
            <param name="quiet">If false, outputs detailed info.</param>
            <returns>maximum normalized difference between analytic and numeric directional derivative over multiple tests</returns>
        </member>
        <member name="F:Microsoft.ML.Numeric.GradientTester.Header">
            <summary>
            The head of the test output
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientTester.TestAllCoords(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Tests the gradient using finite differences on each axis (appropriate for small functions)
            </summary>
            <param name="f"></param>
            <param name="x"></param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientTester.TestCoords(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,System.Collections.Generic.IList{System.Int32})">
            <summary>
            Tests the gradient using finite differences on each axis in the list
            </summary>
            <param name="f">Function to test</param>
            <param name="x">Point at which to test</param>
            <param name="coords">List of coordinates to test</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientTester.Test(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@,System.Boolean,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Tests the gradient reported by <paramref name="f"/>.
            </summary>
            <param name="f">Function to test</param>
            <param name="x">Point at which to test</param>
            <param name="dir">Direction to test derivative</param>
            <param name="quiet">Whether to disable output</param>
            <param name="newGrad">This is a reusable working buffer for intermediate calculations</param>
            <param name="newX">This is a reusable working buffer for intermediate calculations</param>
            <returns>Normalized difference between analytic and numeric directional derivative</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.L1Optimizer">
            <summary>
            Orthant-Wise Limited-memory Quasi-Newton algorithm
            for optimization of smooth convex objectives plus L1-regularization
            If you use this code for published research, please cite
              Galen Andrew and Jianfeng Gao, "Scalable Training of L1-Regularized Log-Linear Models",	ICML 2007
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.L1Optimizer.#ctor(Microsoft.ML.IHostEnvironment,System.Int32,System.Single,System.Int32,System.Boolean,Microsoft.ML.Numeric.ITerminationCriterion,System.Boolean)">
            <summary>
            Create an L1Optimizer with the supplied value of M and termination criterion
            </summary>
            <param name="env">The environment</param>
            <param name="biasCount">Number of biases</param>
            <param name="l1weight">Weight of L1 regularizer</param>
            <param name="m">The number of previous iterations to store</param>
            <param name="keepDense">Whether the optimizer will keep its internal state dense</param>
            <param name="term">Termination criterion</param>
            <param name="enforceNonNegativity">The flag enforcing the non-negativity constraint</param>
        </member>
        <member name="T:Microsoft.ML.Numeric.L1Optimizer.L1OptimizerState">
            <summary>
            Contains information about the state of the optimizer
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.L1Optimizer.L1OptimizerState.EvalCore(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.IProgressChannelProvider)">
            <summary>
            This is the original differentiable function with the injected L1 term.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.L1Optimizer.L1OptimizerState.LineSearch(Microsoft.ML.IChannel,System.Boolean)">
            <summary>
            Backtracking line search with Armijo-like condition, from Andrew &amp; Gao
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.ILineSearch">
            <summary>
            Line search that does not use derivatives
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.ILineSearch.Minimize(System.Func{System.Single,System.Single})">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="func">Function to minimize</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.DiffFunc1D">
            <summary>
            Delegate for differentiable 1-D functions
            </summary>
            <param name="x">Point to evaluate</param>
            <param name="deriv">Derivative at that point</param>
            <returns></returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.IDiffLineSearch">
            <summary>
            Line search that uses derivatives
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.IDiffLineSearch.Minimize(Microsoft.ML.Numeric.DiffFunc1D,System.Single,System.Single)">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="func">Function to minimize</param>
            <param name="initValue">Value of function at 0</param>
            <param name="initDeriv">Derivative of function at 0</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.CubicInterpLineSearch">
            <summary>
            Cubic interpolation line search
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.CubicInterpLineSearch.MaxNumSteps">
            <summary>
            Gets or sets maximum number of steps.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.CubicInterpLineSearch.MinWindow">
            <summary>
            Gets or sets the minimum relative size of bounds around solution.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.CubicInterpLineSearch.MaxStep">
            <summary>
            Gets or sets maximum step size
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.CubicInterpLineSearch.#ctor(System.Int32)">
            <summary>
            Makes a CubicInterpLineSearch
            </summary>
            <param name="maxNumSteps">Maximum number of steps before terminating</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.CubicInterpLineSearch.#ctor(System.Single)">
            <summary>
            Makes a CubicInterpLineSearch
            </summary>
            <param name="minWindow">Minimum relative size of bounds around solution</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.CubicInterpLineSearch.CubicInterp(Microsoft.ML.Numeric.CubicInterpLineSearch.StepValueDeriv,Microsoft.ML.Numeric.CubicInterpLineSearch.StepValueDeriv)">
            <summary>
            Cubic interpolation routine from Nocedal and Wright
            </summary>
            <param name="a">first point, with value and derivative</param>
            <param name="b">second point, with value and derivative</param>
            <returns>local minimum of interpolating cubic polynomial</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.CubicInterpLineSearch.Minimize(Microsoft.ML.Numeric.DiffFunc1D,System.Single,System.Single)">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="func">Function to minimize</param>
            <param name="initValue">Value of function at 0</param>
            <param name="initDeriv">Derivative of function at 0</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.GoldenSectionSearch">
            <summary>
            Finds local minimum with golden section search.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GoldenSectionSearch.MaxNumSteps">
            <summary>
            Gets or sets maximum number of steps before terminating.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GoldenSectionSearch.MinWindow">
            <summary>
            Gets or sets minimum relative size of bounds around solution.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GoldenSectionSearch.MaxStep">
            <summary>
            Gets or sets maximum step size.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.GoldenSectionSearch.#ctor(System.Int32)">
            <summary>
            Makes a new GoldenSectionSearch
            </summary>
            <param name="maxNumSteps">Maximum number of steps before terminating (not including bracketing)</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GoldenSectionSearch.#ctor(System.Single)">
            <summary>
            Makes a new GoldenSectionSearch
            </summary>
            <param name="minWindow">Minimum relative size of bounds around solution</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GoldenSectionSearch.Minimize(Microsoft.ML.Numeric.DiffFunc1D,System.Single,System.Single)">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="f">Function to minimize</param>
            <param name="initVal">Value of function at 0</param>
            <param name="initDeriv">Derivative of function at 0</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.GoldenSectionSearch.Minimize(Microsoft.ML.Numeric.DiffFunc1D)">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="func">Function to minimize</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.GoldenSectionSearch.Minimize(System.Func{System.Single,System.Single})">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="func">Function to minimize</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.BacktrackingLineSearch">
            <summary>
            Backtracking line search with Armijo condition
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.BacktrackingLineSearch.#ctor(System.Single)">
            <summary>
            Makes a backtracking line search
            </summary>
            <param name="c1">Parameter for Armijo condition</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.BacktrackingLineSearch.Minimize(Microsoft.ML.Numeric.DiffFunc1D,System.Single,System.Single)">
            <summary>
            Finds a local minimum of the function
            </summary>
            <param name="f">Function to minimize</param>
            <param name="initVal">Value of function at 0</param>
            <param name="initDeriv">Derivative of function at 0</param>
            <returns>Minimizing value</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.ITerminationCriterion">
            <summary>
            An object which is used to decide whether to stop optimization.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.ITerminationCriterion.FriendlyName">
            <summary>
            Name appropriate for display to the user.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.ITerminationCriterion.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Determines whether to stop optimization
            </summary>
            <param name="state">the state of the optimizer</param>
            <param name="message">a message to be printed (or null for no message)</param>
            <returns>true iff criterion is met, i.e. optimization should halt</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.ITerminationCriterion.Reset">
            <summary>
            Prepares the ITerminationCriterion for a new round of optimization
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.GradientCheckingMonitor">
            <summary>
            A wrapper for a termination criterion that checks the gradient at a specified interval
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientCheckingMonitor.#ctor(Microsoft.ML.Numeric.ITerminationCriterion,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:Microsoft.ML.Numeric.GradientCheckingMonitor"/> class.
            </summary>
            <param name="termCrit">The termination criterion</param>
            <param name="gradientCheckInterval">The gradient check interval.</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientCheckingMonitor.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Determines whether to stop optimization
            </summary>
            <param name="state">the state of the optimizer</param>
            <param name="message">a message to be printed (or null for no message)</param>
            <returns>
            true iff criterion is met, i.e. optimization should halt
            </returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.GradientCheckingMonitor.Reset">
            <summary>
            Prepares the ITerminationCriterion for a new round of optimization
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.StaticTerminationCriterion">
            <summary>
            An abstract partial implementation of ITerminationCriterion for those which do not require resetting
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.StaticTerminationCriterion.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Determines whether to stop optimization
            </summary>
            <param name="state">the state of the optimizer</param>
            <param name="message">a message to be printed (or null for no message)</param>
            <returns>
            true iff criterion is met, i.e. optimization should halt
            </returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.StaticTerminationCriterion.Reset">
            <summary>
            Prepares the ITerminationCriterion for a new round of optimization
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.MeanImprovementCriterion">
            <summary>
            Terminates when the geometrically-weighted average improvement falls below the tolerance
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanImprovementCriterion.#ctor(System.Single,System.Single,System.Int32)">
            <summary>
            Initializes a new instance of the <see cref="T:Microsoft.ML.Numeric.MeanImprovementCriterion"/> class.
            </summary>
            <param name="tol">The tolerance parameter</param>
            <param name="lambda">The geometric weighting factor. Higher means more heavily weighted toward older values.</param>
            <param name="maxIterations">Maximum amount of iteration</param>
        </member>
        <member name="P:Microsoft.ML.Numeric.MeanImprovementCriterion.Tolerance">
            <summary>
            When criterion drops below this value, optimization is terminated
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanImprovementCriterion.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Determines whether to stop optimization
            </summary>
            <param name="state">the state of the optimizer</param>
            <param name="message">a message to be printed (or null for no message)</param>
            <returns>
            true iff criterion is met, i.e. optimization should halt
            </returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanImprovementCriterion.Reset">
            <summary>
            Prepares the ITerminationCriterion for a new round of optimization
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion">
            <summary>
            Stops optimization when the average objective improvement over the last
            n iterations, normalized by the function value, is small enough.
            </summary>
            <remarks>
            Inappropriate for functions whose optimal value is non-positive, because of normalization
            </remarks>
        </member>
        <member name="P:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.Tolerance">
            <summary>
            When criterion drops below this value, optimization is terminated
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.Iters">
            <summary>
            Number of previous iterations to store
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.#ctor(System.Single,System.Int32,System.Int32)">
            <summary>
            Create a MeanRelativeImprovementCriterion
            </summary>
            <param name="tol">tolerance level</param>
            <param name="n">number of past iterations to average over</param>
            <param name="maxIterations">Maximum amount of iteration</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Returns true if the average objective improvement over the last
            n iterations, normalized by the function value, is less than the tolerance
            </summary>
            <param name="state">current state of the optimizer</param>
            <param name="message">the current value of the criterion</param>
            <returns>true if criterion is less than tolerance</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.ToString">
            <summary>
            String summary of criterion
            </summary>
            <returns>summary of criterion</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.MeanRelativeImprovementCriterion.Reset">
            <summary>
            Prepares the ITerminationCriterion for a new round of optimization
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.UpperBoundOnDistanceWithL2">
            <summary>
            Uses the gradient to determine an upper bound on (relative) distance from the optimum.
            </summary>
            <remarks>
            Works if the objective uses L2 prior (or in general if the hessian H is such
            that H > (1 / sigmaSq) * I at all points)
            Inappropriate for functions whose optimal value is non-positive, because of normalization
            </remarks>
        </member>
        <member name="P:Microsoft.ML.Numeric.UpperBoundOnDistanceWithL2.Tolerance">
            <summary>
            When criterion drops below this value, optimization is terminated
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.UpperBoundOnDistanceWithL2.#ctor(System.Single,System.Single)">
            <summary>
            Create termination criterion with supplied value of sigmaSq and tolerance
            </summary>
            <param name="sigmaSq">value of sigmaSq in L2 regularizer</param>
            <param name="tol">tolerance level</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.UpperBoundOnDistanceWithL2.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Returns true if the proved bound on the distance from the optimum,
            normalized by the function value, is less than the tolerance
            </summary>
            <param name="state">current state of the optimizer</param>
            <param name="message">value of criterion</param>
            <returns>true if criterion is less than tolerance</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.UpperBoundOnDistanceWithL2.ToString">
            <summary>
            String summary of criterion
            </summary>
            <returns>summary of criterion</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.RelativeNormGradient">
            <summary>
            Criterion based on the norm of the gradient being small enough
            </summary>
            <remarks>
            Inappropriate for functions whose optimal value is non-positive, because of normalization
            </remarks>
        </member>
        <member name="P:Microsoft.ML.Numeric.RelativeNormGradient.Tolerance">
            <summary>
            When criterion drops below this value, optimization is terminated
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.RelativeNormGradient.#ctor(System.Single)">
            <summary>
            Create a RelativeNormGradient with the supplied tolerance
            </summary>
            <param name="tol">tolerance level</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.RelativeNormGradient.Terminate(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String@)">
            <summary>
            Returns true if the norm of the gradient, divided by the value, is less than the tolerance.
            </summary>
            <param name="state">current state of the optimzer</param>
            <param name="message">the current value of the criterion</param>
            <returns>true iff criterion is less than the tolerance</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.RelativeNormGradient.ToString">
            <summary>
            String summary of criterion
            </summary>
            <returns>summary of criterion</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.Optimizer">
            <summary>
            Limited-memory BFGS quasi-Newton optimization routine
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.Optimizer.EnforceNonNegativity">
            Based on Nocedal and Wright, "Numerical Optimization, Second Edition"
        </member>
        <member name="F:Microsoft.ML.Numeric.Optimizer.Env">
            <summary>
            The host environment to use for reporting progress and exceptions.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.M">
            <summary>
            Number of previous iterations to remember for estimate of Hessian.
            </summary>
            <remarks>
            Higher M means better approximation to Newton's method, but uses more memory,
            and requires more time to compute direction. The optimal setting of M is problem
            specific, depending on such factors as how expensive is function evaluation
            compared to choosing the direction, how easily approximable is the function's
            Hessian, etc.
            M = 15..20 is usually reasonable but if necessary even M=2 is better than
            gradient descent
            </remarks>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.TotalMemoryLimit">
            <summary>
            Gets or sets a bound on the total number of bytes allowed.
            If the whole application is using more than this, no more vectors will be allocated.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.#ctor(Microsoft.ML.IHostEnvironment,System.Int32,System.Boolean,Microsoft.ML.Numeric.ITerminationCriterion,System.Boolean)">
            <summary>
            Create an optimizer with the supplied value of M and termination criterion
            </summary>
            <param name="env">The host environment</param>
            <param name="m">The number of previous iterations to store</param>
            <param name="keepDense">Whether the optimizer will keep its internal state dense</param>
            <param name="term">Termination criterion, defaults to MeanRelativeImprovement if null</param>
            <param name="enforceNonNegativity">The flag enforcing the non-negativity constraint</param>
        </member>
        <member name="T:Microsoft.ML.Numeric.Optimizer.OptimizerException">
            <summary>
            A class for exceptions thrown by the optimizer.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerException.State">
            <summary>
            The state of the optimizer when premature convergence happened.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.Optimizer.OptimizerState">
            <summary>
            Contains information about the state of the optimizer
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.Optimizer.OptimizerState.Dim">
            <summary>
            The dimensionality of the function
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.Function">
            <summary>
            The function being optimized
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.X">
            <summary>
            The current point being explored
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.Grad">
            <summary>
            The gradient at the current point
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.LastDir">
            <summary>
            The direction of search that led to the current point
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.Value">
            <summary>
            The current function value
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.LastValue">
            <summary>
            The function value at the last point
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.Iter">
            <summary>
            The number of iterations so far
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.OptimizerState.GradientCalculations">
            <summary>
            The number of completed gradient calculations in the current iteration.
            </summary>
            <remarks>This is updated in derived classes, since they may call Eval at different times.</remarks>
        </member>
        <member name="F:Microsoft.ML.Numeric.Optimizer.OptimizerState._keepDense">
            <summary>
            Whether the optimizer state will keep its internal vectors dense or not.
            This being true may lead to reduced load on the garbage collector, at the
            cost of possibly higher overall memory utilization.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.OptimizerState.CreateWorkingVector">
            <summary>
            Convenience function to construct a working vector of length <c>Dim</c>.
            </summary>
            <returns></returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.OptimizerState.LineSearch(Microsoft.ML.IChannel,System.Boolean)">
            <summary>
            An implementation of the line search for the Wolfe conditions, from Nocedal &amp; Wright
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.OptimizerState.CubicInterp(Microsoft.ML.Numeric.Optimizer.OptimizerState.PointValueDeriv,Microsoft.ML.Numeric.Optimizer.OptimizerState.PointValueDeriv)">
            <summary>
            Cubic interpolation routine from Nocedal and Wright
            </summary>
            <param name="p0">first point, with value and derivative</param>
            <param name="p1">second point, with value and derivative</param>
            <returns>local minimum of interpolating cubic polynomial</returns>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.Minimize(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single@)">
            <summary>
            Minimize a function using the MeanRelativeImprovement termination criterion with the supplied tolerance level
            </summary>
            <param name="function">The function to minimize</param>
            <param name="initial">The initial point</param>
            <param name="tolerance">Convergence tolerance (smaller means more iterations, closer to exact optimum)</param>
            <param name="result">The point at the optimum</param>
            <param name="optimum">The optimum function value</param>
            <exception cref="T:Microsoft.ML.Numeric.Optimizer.PrematureConvergenceException">Thrown if successive points are within numeric precision of each other, but termination condition is still unsatisfied.</exception>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.Minimize(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single@)">
            <summary>
            Minimize a function.
            </summary>
            <param name="function">The function to minimize</param>
            <param name="initial">The initial point</param>
            <param name="result">The point at the optimum</param>
            <param name="optimum">The optimum function value</param>
            <exception cref="T:Microsoft.ML.Numeric.Optimizer.PrematureConvergenceException">Thrown if successive points are within numeric precision of each other, but termination condition is still unsatisfied.</exception>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.Minimize(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Numeric.ITerminationCriterion,Microsoft.ML.Data.VBuffer{System.Single}@,System.Single@)">
            <summary>
            Minimize a function using the supplied termination criterion
            </summary>
            <param name="function">The function to minimize</param>
            <param name="initial">The initial point</param>
            <param name="term">termination criterion to use</param>
            <param name="result">The point at the optimum</param>
            <param name="optimum">The optimum function value</param>
            <exception cref="T:Microsoft.ML.Numeric.Optimizer.PrematureConvergenceException">Thrown if successive points are within numeric precision of each other, but termination condition is still unsatisfied.</exception>
        </member>
        <member name="T:Microsoft.ML.Numeric.Optimizer.PrematureConvergenceException">
            <summary>
            This exception is thrown if successive differences between points
            reach the limits of numerical stability, but the termination condition
            still hasn't been satisfied
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.Optimizer.PrematureConvergenceException.#ctor(Microsoft.ML.Numeric.Optimizer.OptimizerState,System.String)">
            <summary>
            Makes a PrematureConvergenceException with the supplied message
            </summary>
            <param name="state">The OptimizerState when the exception was thrown</param>
            <param name="message">message for exception</param>
        </member>
        <member name="P:Microsoft.ML.Numeric.Optimizer.Quiet">
            <summary>
            If true, suppresses all output.
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.DTerminate">
            <summary>
            Delegate for functions that determine whether to terminate search. Called after each update.
            </summary>
            <param name="x">Current iterate</param>
            <returns>True if search should terminate</returns>
        </member>
        <member name="T:Microsoft.ML.Numeric.SgdOptimizer">
            <summary>
            Stochastic gradient descent with variations (minibatch, momentum, averaging).
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.BatchSize">
            <summary>
            Size of minibatches
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.Momentum">
            <summary>
            Momentum parameter
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.T0">
            <summary>
            Base of step size schedule s_t = 1 / (t0 + f(t))
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.SgdOptimizer._terminate">
            <summary>
            Termination criterion
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.Averaging">
            <summary>
            If true, iterates are averaged
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.RateSchedule">
            <summary>
            Gets/Sets rate schedule type
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.SgdOptimizer.MaxSteps">
            <summary>
            Gets/Sets maximum number of steps. Set to 0 for no max
            </summary>
        </member>
        <member name="T:Microsoft.ML.Numeric.SgdOptimizer.RateScheduleType">
            <summary>
            Annealing schedule for learning rate
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.SgdOptimizer.RateScheduleType.Constant">
            <summary>
            r_t = 1 / t0
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.SgdOptimizer.RateScheduleType.Sqrt">
            <summary>
            r_t = 1 / (t0 + sqrt(t))
            </summary>
        </member>
        <member name="F:Microsoft.ML.Numeric.SgdOptimizer.RateScheduleType.Linear">
            <summary>
            r_t = 1 / (t0 + t)
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.SgdOptimizer.#ctor(Microsoft.ML.Numeric.DTerminate,Microsoft.ML.Numeric.SgdOptimizer.RateScheduleType,System.Boolean,System.Single,System.Int32,System.Single,System.Int32)">
            <summary>
            Creates SGDOptimizer and sets optimization parameters
            </summary>
            <param name="terminate">Termination criterion</param>
            <param name="rateSchedule">Annealing schedule type for learning rate</param>
            <param name="averaging">If true, all iterates are averaged</param>
            <param name="t0">Base for learning rate schedule</param>
            <param name="batchSize">Average this number of stochastic gradients for each update</param>
            <param name="momentum">Momentum parameter</param>
            <param name="maxSteps">Maximum number of updates (0 for no max)</param>
        </member>
        <member name="T:Microsoft.ML.Numeric.SgdOptimizer.DStochasticGradient">
            <summary>
            Delegate for functions to query stochastic gradient at a point
            </summary>
            <param name="x">Point at which to evaluate</param>
            <param name="grad">Vector to be filled in with gradient</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.SgdOptimizer.Minimize(Microsoft.ML.Numeric.SgdOptimizer.DStochasticGradient,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Minimize the function represented by <paramref name="f"/>.
            </summary>
            <param name="f">Stochastic gradients of function to minimize</param>
            <param name="initial">Initial point</param>
            <param name="result">Approximate minimum of <paramref name="f"/></param>
        </member>
        <member name="T:Microsoft.ML.Numeric.GDOptimizer">
            <summary>
            Deterministic gradient descent with line search
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GDOptimizer.LineSearch">
            <summary>
            Line search to use.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GDOptimizer.MaxSteps">
            <summary>
            Gets/Sets maximum number of steps. Set to 0 for no max.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GDOptimizer.Terminate">
            <summary>
            Gets/sets termination criterion.
            </summary>
        </member>
        <member name="P:Microsoft.ML.Numeric.GDOptimizer.UseCG">
            <summary>
            Gets/sets whether to use nonlinear conjugate gradient.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.GDOptimizer.#ctor(Microsoft.ML.Numeric.DTerminate,Microsoft.ML.Numeric.IDiffLineSearch,System.Boolean,System.Int32)">
            <summary>
            Makes a new GDOptimizer with the given optimization parameters
            </summary>
            <param name="terminate">Termination criterion</param>
            <param name="lineSearch">Line search to use</param>
            <param name="maxSteps">Maximum number of updates</param>
            <param name="useCG">Use Cubic interpolation line search or Backtracking line search with Armijo condition</param>
        </member>
        <member name="M:Microsoft.ML.Numeric.GDOptimizer.Minimize(Microsoft.ML.Numeric.DifferentiableFunction,Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Finds approximate minimum of the function
            </summary>
            <param name="function">Function to minimize</param>
            <param name="initial">Initial point</param>
            <param name="result">Approximate minimum</param>
        </member>
        <member name="T:Microsoft.ML.Numeric.TerminateTester">
            <summary>
            Terminates the optimization if NA value appears in result or no progress is made.
            </summary>
        </member>
        <member name="M:Microsoft.ML.Numeric.TerminateTester.ShouldTerminate(Microsoft.ML.Data.VBuffer{System.Single}@,Microsoft.ML.Data.VBuffer{System.Single}@)">
            <summary>
            Test whether the optimization should terminate. Returns true if x contains NA or +/-Inf or x equals xprev.
            </summary>
            <param name="x">The current value.</param>
            <param name="xprev">The value from the previous iteration.</param>
            <returns>True if the optimization routine should terminate at this iteration.</returns>
        </member>
        <member name="T:Microsoft.ML.StandardLearnersCatalog">
            <summary>
            TrainerEstimator extension methods.
            </summary>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticGradientDescent(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Double,System.Single)">
            <summary>
            Predict a target using a linear classification model trained with <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainer"/>.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column, or dependent variable.</param>
            <param name="featureColumnName">The features, or independent variables.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="maxIterations">The maximum number of passes through the training dataset; set to 1 to simulate online learning.</param>
            <param name="initLearningRate">The initial <a href="tmpurl_lr">learning rate</a> used by SGD.</param>
            <param name="l2Weight">The L2 weight for <a href='tmpurl_regularization'>regularization</a>.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
            [!code-csharp[StochasticGradientDescent](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescent.cs)]
            ]]>
            </format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticGradientDescent(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdBinaryTrainer.Options)">
            <summary>
            Predict a target using a linear classification model trained with <see cref="T:Microsoft.ML.Trainers.SgdBinaryTrainer"/> and advanced options.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Trainer options.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
            [!code-csharp[StochasticGradientDescentWithOptions](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/StochasticGradientDescentWithOptions.cs)]
            ]]>
            </format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticGradientDescentNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.IClassificationLoss,System.Int32,System.Double,System.Single)">
            <summary>
             Predict a target using a linear classification model trained with <see cref="T:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer"/>.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column, or dependent variable.</param>
            <param name="featureColumnName">The features, or independent variables.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="loss">The loss function minimized in the training process. Using, for example, <see cref="T:Microsoft.ML.HingeLoss"/> leads to a support vector machine trainer.</param>
            <param name="maxIterations">The maximum number of passes through the training dataset; set to 1 to simulate online learning.</param>
            <param name="initLearningRate">The initial <a href="tmpurl_lr">learning rate</a> used by SGD.</param>
            <param name="l2Weight">The L2 weight for <a href='tmpurl_regularization'>regularization</a>.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticGradientDescentNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer.Options)">
            <summary>
            Predict a target using a linear classification model trained with <see cref="T:Microsoft.ML.Trainers.SgdNonCalibratedBinaryTrainer"/> and advanced options.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Trainer options.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaRegressionLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Predict a target using a linear regression model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
            <param name="loss">The custom loss, if unspecified will be <see cref="T:Microsoft.ML.SquaredLoss"/>.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.SdcaRegressionTrainer.Options)">
            <summary>
            Predict a target using a linear regression model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Predict a target using a logistic regression model trained with the SDCA trainer.
            The trained model can produce probability by feeding the output value of the linear
            function to a <see cref="T:Microsoft.ML.Calibrators.PlattCalibrator"/>.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCALogisticRegression.cs)]
            ]]></format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SdcaBinaryTrainer.Options)">
            <summary>
            Predict a target using a logistic regression model trained with the SDCA trainer.
            The trained model can produce probability via feeding output value of the linear
            function to a <see cref="T:Microsoft.ML.Calibrators.PlattCalibrator"/>. Compared with <see cref="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})"/>,
            this function allows more advanced settings by accepting <see cref="T:Microsoft.ML.Trainers.SdcaBinaryTrainer.Options"/>.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscentNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaClassificationLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Predict a target using a linear binary classification model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="loss">The custom loss. Defaults to log-loss if not specified.</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[SDCA](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/SDCASupportVectorMachine.cs)]
            ]]></format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscentNonCalibrated(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.SdcaNonCalibratedBinaryTrainer.Options)">
            <summary>
            Predict a target using a linear binary classification model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.ISupportSdcaClassificationLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})">
            <summary>
            Predict a target using a linear multiclass classification model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The multiclass classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="loss">The optional custom loss.</param>
            <param name="l2Const">The L2 regularization hyperparameter.</param>
            <param name="l1Threshold">The L1 regularization hyperparameter. Higher values will tend to lead to more sparse model.</param>
            <param name="maxIterations">The maximum number of passes to perform over the data.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.StochasticDualCoordinateAscent(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.SdcaMultiClassTrainer.Options)">
            <summary>
            Predict a target using a linear multiclass classification model trained with the SDCA trainer.
            </summary>
            <param name="catalog">The multiclass classification catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.AveragedPerceptron(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,Microsoft.ML.IClassificationLoss,System.Single,System.Boolean,System.Single,System.Int32)">
            <summary>
            Predict a target using a linear binary classification model trained with <see cref="T:Microsoft.ML.Trainers.AveragedPerceptronTrainer"/>.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="lossFunction">A custom <a href="tmpurl_loss">loss</a>. If <see langword="null"/>, hinge loss will be used resulting in max-margin averaged perceptron.</param>
            <param name="learningRate"><a href="tmpurl_lr">Learning rate</a>.</param>
            <param name="decreaseLearningRate">
            <see langword="true" /> to decrease the <paramref name="learningRate"/> as iterations progress; otherwise, <see langword="false" />.
            Default is <see langword="false" />.
            </param>
            <param name="l2RegularizerWeight">The L2 weight for <a href='tmpurl_regularization'>regularization</a>.</param>
            <param name="numIterations">Number of passes through the training dataset.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
            [!code-csharp[AveragedPerceptron](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptron.cs)]
            ]]>
            </format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.AveragedPerceptron(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.AveragedPerceptronTrainer.Options)">
            <summary>
            Predict a target using a linear binary classification model trained with <see cref="T:Microsoft.ML.Trainers.AveragedPerceptronTrainer"/> and advanced options.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Trainer options.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
            [!code-csharp[AveragedPerceptron](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/BinaryClassification/AveragedPerceptronWithOptions.cs)]
            ]]>
            </format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.OnlineGradientDescent(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,Microsoft.ML.IRegressionLoss,System.Single,System.Boolean,System.Single,System.Int32)">
            <summary>
            Predict a target using a linear regression model trained with the <see cref="T:Microsoft.ML.Trainers.OnlineGradientDescentTrainer"/> trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="lossFunction">The custom loss. Defaults to <see cref="T:Microsoft.ML.SquaredLoss"/> if not provided.</param>
            <param name="learningRate">The learning Rate.</param>
            <param name="decreaseLearningRate">Decrease learning rate as iterations progress.</param>
            <param name="l2RegularizerWeight">L2 regularization weight.</param>
            <param name="numIterations">Number of training iterations through the data.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.OnlineGradientDescent(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.OnlineGradientDescentTrainer.Options)">
            <summary>
            Predict a target using a linear regression model trained with the <see cref="T:Microsoft.ML.Trainers.OnlineGradientDescentTrainer"/> trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LogisticRegression(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
             Predict a target using a linear binary classification model trained with the <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/> trainer.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
            <param name="l1Weight">Weight of L1 regularization term.</param>
            <param name="l2Weight">Weight of L2 regularization term.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/>. Low=faster, less accurate.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
            [!code-csharp[Logistic Regression](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/LogisticRegression.cs)]
            ]]>
            </format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LogisticRegression(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.LogisticRegression.Options)">
            <summary>
             Predict a target using a linear binary classification model trained with the <see cref="T:Microsoft.ML.Trainers.LogisticRegression"/> trainer.
            </summary>
            <param name="catalog">The binary classification catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.PoissonRegression(Microsoft.ML.RegressionCatalog.RegressionTrainers,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
            Predict a target using a linear regression model trained with the <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/> trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="l1Weight">Weight of L1 regularization term.</param>
            <param name="l2Weight">Weight of L2 regularization term.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/>. Low=faster, less accurate.</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.PoissonRegression(Microsoft.ML.RegressionCatalog.RegressionTrainers,Microsoft.ML.Trainers.PoissonRegression.Options)">
            <summary>
            Predict a target using a linear regression model trained with the <see cref="T:Microsoft.ML.Trainers.PoissonRegression"/> trainer.
            </summary>
            <param name="catalog">The regression catalog trainer object.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LogisticRegression(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String,System.String,System.Single,System.Single,System.Single,System.Int32,System.Boolean)">
            <summary>
            Predict a target using a linear multiclass classification model trained with the <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/> trainer.
            </summary>
            <param name="catalog">The <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="enforceNoNegativity">Enforce non-negative weights.</param>
            <param name="l1Weight">Weight of L1 regularization term.</param>
            <param name="l2Weight">Weight of L2 regularization term.</param>
            <param name="memorySize">Memory size for <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/>. Low=faster, less accurate.</param>
            <param name="optimizationTolerance">Threshold for optimizer convergence.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LogisticRegression(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.MulticlassLogisticRegression.Options)">
            <summary>
            Predict a target using a linear multiclass classification model trained with the <see cref="T:Microsoft.ML.Trainers.MulticlassLogisticRegression"/> trainer.
            </summary>
            <param name="catalog">The <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.NaiveBayes(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String)">
            <summary>
            Predicts a target using a linear multiclass classification model trained with the <see cref="T:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer"/>.
            The <see cref="T:Microsoft.ML.Trainers.MultiClassNaiveBayesTrainer"/> trains a multiclass Naive Bayes predictor that supports binary feature values.
            </summary>
            <param name="catalog">The <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
            <param name="labelColumnName">The name of the label column.</param>
            <param name="featureColumnName">The name of the feature column.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.GetCalibratorTrainerOrThrow(Microsoft.ML.IExceptionContext,Microsoft.ML.IEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.Calibrators.ICalibrator}})">
            <summary>
            Works via the <see cref="T:Microsoft.ML.Calibrators.IHaveCalibratorTrainer"/> shim interface to extract from the calibrating training
            estimator the internal <see cref="T:Microsoft.ML.Calibrators.ICalibratorTrainer"/> object. Note that this should be a temporary measure,
            since the trainers should really be changed to actually work over estimators.
            </summary>
            <param name="ectx">The exception context.</param>
            <param name="calibratorEstimator">The estimator out of which we should try to extract the calibrator trainer.</param>
            <returns>The calibrator trainer.</returns>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.OneVersusAll``1(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.ITrainerEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{``0},``0},System.String,System.Boolean,Microsoft.ML.IEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.Calibrators.ICalibrator}},System.Int32,System.Boolean)">
            <summary>
            Predicts a target using a linear multiclass classification model trained with the <see cref="T:Microsoft.ML.Trainers.Ova"/>.
            </summary>
            <remarks>
            <para>
            In <see cref="T:Microsoft.ML.Trainers.Ova"/> In this strategy, a binary classification algorithm is used to train one classifier for each class,
            which distinguishes that class from all other classes. Prediction is then performed by running these binary classifiers,
            and choosing the prediction with the highest confidence score.
            </para>
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
            <param name="binaryEstimator">An instance of a binary <see cref="T:Microsoft.ML.Trainers.ITrainerEstimator`2"/> used as the base trainer.</param>
            <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="T:Microsoft.ML.Calibrators.PlattCalibratorTrainer"/></param>
            <param name="labelColumnName">The name of the label colum.</param>
            <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
            <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
            <param name="useProbabilities">Use probabilities (vs. raw outputs) to identify top-score category.</param>
            <typeparam name="TModel">The type of the model. This type parameter will usually be inferred automatically from <paramref name="binaryEstimator"/>.</typeparam>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.PairwiseCoupling``1(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.ITrainerEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{``0},``0},System.String,System.Boolean,Microsoft.ML.IEstimator{Microsoft.ML.ISingleFeaturePredictionTransformer{Microsoft.ML.Calibrators.ICalibrator}},System.Int32)">
            <summary>
            Predicts a target using a linear multiclass classification model trained with the <see cref="T:Microsoft.ML.Trainers.Pkpd"/>.
            </summary>
            <remarks>
            <para>
            In the Pairwise coupling (PKPD) strategy, a binary classification algorithm is used to train one classifier for each pair of classes.
            Prediction is then performed by running these binary classifiers, and computing a score for each class by counting how many of the binary
            classifiers predicted it. The prediction is the class with the highest score.
            </para>
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers"/>.</param>
            <param name="binaryEstimator">An instance of a binary <see cref="T:Microsoft.ML.Trainers.ITrainerEstimator`2"/> used as the base trainer.</param>
            <param name="calibrator">The calibrator. If a calibrator is not explicitely provided, it will default to <see cref="T:Microsoft.ML.Calibrators.PlattCalibratorTrainer"/></param>
            <param name="labelColumnName">The name of the label colum.</param>
            <param name="imputeMissingLabelsAsNegative">Whether to treat missing labels as having negative labels, instead of keeping them missing.</param>
            <param name="maxCalibrationExamples">Number of instances to train the calibrator.</param>
            <typeparam name="TModel">The type of the model. This type parameter will usually be inferred automatically from <paramref name="binaryEstimator"/>.</typeparam>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LinearSupportVectorMachines(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32)">
            <summary>
            Predict a target using a linear binary classification model trained with the <see cref="T:Microsoft.ML.Trainers.LinearSvmTrainer"/> trainer.
            </summary>
            <remarks>
            <para>
            The idea behind support vector machines, is to map instances into a high dimensional space
            in which the two classes are linearly separable, i.e., there exists a hyperplane such that all the positive examples are on one side of it,
            and all the negative examples are on the other.
            </para>
            <para>
            After this mapping, quadratic programming is used to find the separating hyperplane that maximizes the
            margin, i.e., the minimal distance between it and the instances.
            </para>
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.BinaryClassificationCatalog"/>.</param>
            <param name="labelColumnName">The name of the label column. </param>
            <param name="featureColumnName">The name of the feature column.</param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <param name="numIterations">The number of training iteraitons.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.LinearSupportVectorMachines(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.LinearSvmTrainer.Options)">
            <summary>
            Predict a target using a linear binary classification model trained with the <see cref="T:Microsoft.ML.Trainers.LinearSvmTrainer"/> trainer.
            </summary>
            <remarks>
            <para>
            The idea behind support vector machines, is to map instances into a high dimensional space
            in which the two classes are linearly separable, i.e., there exists a hyperplane such that all the positive examples are on one side of it,
            and all the negative examples are on the other.
            </para>
            <para>
            After this mapping, quadratic programming is used to find the separating hyperplane that maximizes the
            margin, i.e., the minimal distance between it and the instances.
            </para>
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.BinaryClassificationCatalog"/>.</param>
            <param name="options">Advanced arguments to the algorithm.</param>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.Random(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers)">
            <summary>
            Predict a target using the random binary classification model <see cref="T:Microsoft.ML.Trainers.RandomTrainer"/>.
            </summary>
            <remarks>
            This trainer can be used as a baseline for other more sophisticated mdels.
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.BinaryClassificationCatalog"/>.</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/RandomTrainerSample.cs)]
            ]]></format>
            </example>
        </member>
        <member name="M:Microsoft.ML.StandardLearnersCatalog.Prior(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String)">
            <summary>
            Predict a target using a binary classification model trained with <see cref="T:Microsoft.ML.Trainers.PriorTrainer"/> trainer.
            </summary>
            <remarks>
            This trainer uses the proportion of a label in the training set as the probability of that label.
            This trainer is often used as a baseline for other more sophisticated mdels.
            </remarks>
            <param name="catalog">The <see cref="T:Microsoft.ML.BinaryClassificationCatalog"/>.</param>
            <param name="labelColumnName">The name of the label column. </param>
            <param name="exampleWeightColumnName">The name of the example weight column (optional).</param>
            <example>
            <format type="text/markdown">
            <![CDATA[
             [!code-csharp[FastTree](~/../docs/samples/docs/samples/Microsoft.ML.Samples/Dynamic/Trainers/PriorTrainerSample.cs)]
            ]]></format>
            </example>
        </member>
    </members>
</doc>