StandardTrainersCatalog Class

Definition

public static class StandardTrainersCatalog
type StandardTrainersCatalog = class
Public Module StandardTrainersCatalog
Inheritance
StandardTrainersCatalog

Methods

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, AveragedPerceptronTrainer+Options)

Create an AveragedPerceptronTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data.

AveragedPerceptron(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, IClassificationLoss, Single, Boolean, Single, Int32)

Create an AveragedPerceptronTrainer, which predicts a target using a linear binary classification model trained over boolean label data.

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, LbfgsLogisticRegressionBinaryTrainer+Options)

Create LbfgsLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data.

LbfgsLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Create LbfgsLogisticRegressionBinaryTrainer, which predicts a target using a linear binary classification model trained over boolean label data.

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, LbfgsMaximumEntropyMulticlassTrainer+Options)

Create LbfgsMaximumEntropyMulticlassTrainer with advanced options, which predicts a target using a maximum entropy classification model trained with the L-BFGS method.

LbfgsMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Create LbfgsMaximumEntropyMulticlassTrainer, which predicts a target using a maximum entropy classification model trained with the L-BFGS method.

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, LbfgsPoissonRegressionTrainer+Options)

Create LbfgsPoissonRegressionTrainer using advanced options, which predicts a target using a linear regression model.

LbfgsPoissonRegression(RegressionCatalog+RegressionTrainers, String, String, String, Single, Single, Single, Int32, Boolean)

Create LbfgsPoissonRegressionTrainer, which predicts a target using a linear regression model.

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, LinearSvmTrainer+Options)

Create LinearSvmTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data.

LinearSvm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32)

Create LinearSvmTrainer, which predicts a target using a linear binary classification model trained over boolean label data.

NaiveBayes(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String)

Create a NaiveBayesMulticlassTrainer, which predicts a multiclass target using a Naive Bayes model that supports binary feature values.

OneVersusAll<TModel>(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ITrainerEstimator<BinaryPredictionTransformer<TModel>,TModel>, String, Boolean, IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>, Int32, Boolean)

Create a OneVersusAllTrainer, which predicts a multiclass target using one-versus-all strategy with the binary classification estimator specified by binaryEstimator.

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, OnlineGradientDescentTrainer+Options)

Create OnlineGradientDescentTrainer using advanced options, which predicts a target using a linear regression model.

OnlineGradientDescent(RegressionCatalog+RegressionTrainers, String, String, IRegressionLoss, Single, Boolean, Single, Int32)

Create OnlineGradientDescentTrainer, which predicts a target using a linear regression model.

PairwiseCoupling<TModel>(MulticlassClassificationCatalog+MulticlassClassificationTrainers, ITrainerEstimator<ISingleFeaturePredictionTransformer<TModel>,TModel>, String, Boolean, IEstimator<ISingleFeaturePredictionTransformer<ICalibrator>>, Int32)

Create a PairwiseCouplingTrainer, which predicts a multiclass target using pairwise coupling strategy with the binary classification estimator specified by binaryEstimator.

Prior(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String)

Create PriorTrainer, which predict a target using a binary classification model.

Sdca(RegressionCatalog+RegressionTrainers, SdcaRegressionTrainer+Options)

Create SdcaRegressionTrainer with advanced options, which predicts a target using a linear regression model.

Sdca(RegressionCatalog+RegressionTrainers, String, String, String, ISupportSdcaRegressionLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Create SdcaRegressionTrainer, which predicts a target using a linear regression model.

SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaLogisticRegressionBinaryTrainer+Options)

Create SdcaLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear classification model.

SdcaLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Create SdcaLogisticRegressionBinaryTrainer, which predicts a target using a linear classification model.

SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaMaximumEntropyMulticlassTrainer+Options)

Create SdcaMaximumEntropyMulticlassTrainer with advanced options, which predicts a target using a maximum entropy classification model trained with a coordinate descent method.

SdcaMaximumEntropy(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Create SdcaMaximumEntropyMulticlassTrainer, which predicts a target using a maximum entropy classification model trained with a coordinate descent method.

SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SdcaNonCalibratedBinaryTrainer+Options)

Create SdcaNonCalibratedBinaryTrainer with advanced options, which predicts a target using a linear classification model trained over boolean label data.

SdcaNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Create SdcaNonCalibratedBinaryTrainer, which predicts a target using a linear classification model.

SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, SdcaNonCalibratedMulticlassTrainer+Options)

Create SdcaNonCalibratedMulticlassTrainer with advanced options, which predicts a target using a linear multiclass classification model trained with a coordinate descent method.

SdcaNonCalibrated(MulticlassClassificationCatalog+MulticlassClassificationTrainers, String, String, String, ISupportSdcaClassificationLoss, Nullable<Single>, Nullable<Single>, Nullable<Int32>)

Create SdcaNonCalibratedMulticlassTrainer, which predicts a target using a linear multiclass classification model trained with a coordinate descent method.

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdCalibratedTrainer+Options)

Create SgdCalibratedTrainer with advanced options, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.

SgdCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Double, Single)

Create SgdCalibratedTrainer, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.

SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, SgdNonCalibratedTrainer+Options)

Create SgdNonCalibratedTrainer with advanced options, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.

SgdNonCalibrated(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, IClassificationLoss, Int32, Double, Single)

Create SgdNonCalibratedTrainer, which predicts a target using a linear classification model. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function.

Applies to