TreeExtensions Class

  • Reference

Definition

Namespace:
Microsoft.ML
Assembly:
Microsoft.ML.FastTree.dll
Package:
Microsoft.ML.FastTree v3.0.1
Package:
Microsoft.ML.FastTree v1.0.0
Package:
Microsoft.ML.FastTree v1.1.0
Package:
Microsoft.ML.FastTree v1.2.0
Package:
Microsoft.ML.FastTree v1.3.1
Package:
Microsoft.ML.FastTree v1.4.0
Package:
Microsoft.ML.FastTree v1.5.5
Package:
Microsoft.ML.FastTree v1.6.0
Package:
Microsoft.ML.FastTree v1.7.0
Package:
Microsoft.ML.FastTree v2.0.0

Collection of extension methods used by RegressionCatalog, BinaryClassificationCatalog, MulticlassClassificationCatalog, RankingCatalog, and TransformsCatalog to create instances of decision tree trainers and featurizers.

public static class TreeExtensions
type TreeExtensions = class
Public Module TreeExtensions
Inheritance
Object
TreeExtensions

Methods

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.
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(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.
FastTree(RankingCatalog+RankingTrainers, FastTreeRankingTrainer+Options)

Create a FastTreeRankingTrainer with advanced options, which ranks a series of inputs based on their relevance, using a decision tree ranking model.
FastTree(RankingCatalog+RankingTrainers, String, String, String, String, Int32, Int32, Int32, Double)

Create a FastTreeRankingTrainer, which ranks a series of inputs based on their relevancee, using a decision tree ranking 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.
FeaturizeByFastForestBinary(TransformsCatalog, FastForestBinaryFeaturizationEstimator+Options)

Create FastForestBinaryFeaturizationEstimator, which uses FastForestBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByFastForestRegression(TransformsCatalog, FastForestRegressionFeaturizationEstimator+Options)

Create FastForestRegressionFeaturizationEstimator, which uses FastForestRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByFastTreeBinary(TransformsCatalog, FastTreeBinaryFeaturizationEstimator+Options)

Create FastTreeBinaryFeaturizationEstimator, which uses FastTreeBinaryTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByFastTreeRanking(TransformsCatalog, FastTreeRankingFeaturizationEstimator+Options)

Create FastTreeRankingFeaturizationEstimator, which uses FastTreeRankingTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByFastTreeRegression(TransformsCatalog, FastTreeRegressionFeaturizationEstimator+Options)

Create FastTreeRegressionFeaturizationEstimator, which uses FastTreeRegressionTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByFastTreeTweedie(TransformsCatalog, FastTreeTweedieFeaturizationEstimator+Options)

Create FastTreeTweedieFeaturizationEstimator, which uses FastTreeTweedieTrainer to train TreeEnsembleModelParameters to create tree-based features.
FeaturizeByPretrainTreeEnsemble(TransformsCatalog, PretrainedTreeFeaturizationEstimator+Options)

Create PretrainedTreeFeaturizationEstimator, which produces tree-based features given a TreeEnsembleModelParameters.
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).
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