RegressionCatalog.RegressionTrainers Class

Definition

Class used by MLContext to create instances of regression trainers.

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

Extension Methods

LightGbm(RegressionCatalog+RegressionTrainers, LightGbmRegressionTrainer+Options)

Create LightGbmRegressionTrainer using advanced options, which predicts a target using a gradient boosting decision tree regression model.

LightGbm(RegressionCatalog+RegressionTrainers, String, String, String, Nullable<Int32>, Nullable<Int32>, Nullable<Double>, Int32)

Create LightGbmRegressionTrainer, which predicts a target using a gradient boosting decision tree regression model.

Ols(RegressionCatalog+RegressionTrainers, OlsTrainer+Options)

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

Ols(RegressionCatalog+RegressionTrainers, String, String, String)

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

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.

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.

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.

FastForest(RegressionCatalog+RegressionTrainers, FastForestRegressionTrainer+Options)

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

FastForest(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32)

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

FastTree(RegressionCatalog+RegressionTrainers, FastTreeRegressionTrainer+Options)

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

FastTree(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)

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

FastTreeTweedie(RegressionCatalog+RegressionTrainers, FastTreeTweedieTrainer+Options)

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

FastTreeTweedie(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Int32, Double)

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

Gam(RegressionCatalog+RegressionTrainers, GamRegressionTrainer+Options)

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

Gam(RegressionCatalog+RegressionTrainers, String, String, String, Int32, Int32, Double)

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

Applies to