BinaryClassificationCatalog.BinaryClassificationTrainers Class

Definition

Class used by MLContext to create instances of binary classification trainers.

public sealed class BinaryClassificationCatalog.BinaryClassificationTrainers : Microsoft.ML.TrainCatalogBase.CatalogInstantiatorBase
type BinaryClassificationCatalog.BinaryClassificationTrainers = class
    inherit TrainCatalogBase.CatalogInstantiatorBase
Public NotInheritable Class BinaryClassificationCatalog.BinaryClassificationTrainers
Inherits TrainCatalogBase.CatalogInstantiatorBase
Inheritance
BinaryClassificationCatalog.BinaryClassificationTrainers

Extension Methods

FieldAwareFactorizationMachine(BinaryClassificationCatalog+BinaryClassificationTrainers, FieldAwareFactorizationMachineTrainer+Options)

Create FieldAwareFactorizationMachineTrainer using advanced options, which predicts a target using a field-aware factorization machine trained over boolean label data.

FieldAwareFactorizationMachine(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String)

Create FieldAwareFactorizationMachineTrainer, which predicts a target using a field-aware factorization machine trained over boolean label data.

FieldAwareFactorizationMachine(BinaryClassificationCatalog+BinaryClassificationTrainers, String[], String, String)

Create FieldAwareFactorizationMachineTrainer, which predicts a target using a field-aware factorization machine trained over boolean label data.

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, LightGbmBinaryTrainer+Options)

Create LightGbmBinaryTrainer with advanced options, which predicts a target using a gradient boosting decision tree binary classification.

LightGbm(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Create LightGbmBinaryTrainer, which predicts a target using a gradient boosting decision tree binary classification.

SymbolicSgdLogisticRegression(BinaryClassificationCatalog+BinaryClassificationTrainers, SymbolicSgdLogisticRegressionBinaryTrainer+Options)

Create SymbolicSgdLogisticRegressionBinaryTrainer with advanced options, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution.

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

Create SymbolicSgdLogisticRegressionBinaryTrainer, which predicts a target using a linear binary classification model trained over boolean label data. Stochastic gradient descent (SGD) is an iterative algorithm that optimizes a differentiable objective function. The SymbolicSgdLogisticRegressionBinaryTrainer parallelizes SGD using symbolic execution.

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.

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.

Prior(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String)

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

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.

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.

FastForest(BinaryClassificationCatalog+BinaryClassificationTrainers, FastForestBinaryTrainer+Options)

Create FastForestBinaryTrainer with advanced options, which predicts a target using a decision tree regression model.

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

Create FastForestBinaryTrainer, which predicts a target using a decision tree regression model.

FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, FastTreeBinaryTrainer+Options)

Create FastTreeBinaryTrainer with advanced options, which predicts a target using a decision tree binary classification model.

FastTree(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Int32, Double)

Create FastTreeBinaryTrainer, which predicts a target using a decision tree binary classification model.

Gam(BinaryClassificationCatalog+BinaryClassificationTrainers, GamBinaryTrainer+Options)

Create GamBinaryTrainer using advanced options, which predicts a target using generalized additive models (GAM).

Gam(BinaryClassificationCatalog+BinaryClassificationTrainers, String, String, String, Int32, Int32, Double)

Create GamBinaryTrainer, which predicts a target using generalized additive models (GAM).

Applies to