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Microsoft.ML.Trainers.FastTree Namespace

Namespace containing trainers, model parameters, and utilities for Fast Tree algorithms.

Classes

BoostedTreeOptions

Options for boosting tree trainers.

BoostingFastTreeTrainerBase<TOptions,TTransformer,TModel>
ConsecutiveGeneralityLossRule

Consecutive Loss in Generality (UP).

EarlyStoppingRule
EarlyStoppingRuleBase

Early stopping rule used to terminate training process once meeting a specified criterion. Used for setting EarlyStoppingRuleEarlyStoppingRule.

FastForestBinaryFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastForestBinaryFeaturizationEstimator.Options

Options for the FastForestBinaryFeaturizationEstimator.

FastForestBinaryModelParameters

Model parameters for FastForestBinaryTrainer.

FastForestBinaryTrainer

The IEstimator<TTransformer> for training a decision tree binary classification model using Fast Forest.

FastForestBinaryTrainer.Options

Options for the FastForestBinaryTrainer as used in FastForest(Options).

FastForestOptionsBase

Base class for fast forest trainer options.

FastForestRegressionFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastForestRegressionFeaturizationEstimator.Options

Options for the FastForestRegressionFeaturizationEstimator.

FastForestRegressionModelParameters

Model parameters for FastForestRegressionTrainer.

FastForestRegressionTrainer

The IEstimator<TTransformer> for training a decision tree regression model using Fast Forest.

FastForestRegressionTrainer.Options

Options for the FastForestRegressionTrainer as used in FastForest(Options).

FastTreeBinaryFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastTreeBinaryFeaturizationEstimator.Options

Options for the FastTreeBinaryFeaturizationEstimator.

FastTreeBinaryModelParameters

Model parameters for FastTreeBinaryTrainer.

FastTreeBinaryTrainer

The IEstimator<TTransformer> for training a decision tree binary classification model using FastTree.

FastTreeBinaryTrainer.Options

Options for the FastTreeBinaryTrainer as used in FastTree(Options).

FastTreeRankingFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastTreeRankingFeaturizationEstimator.Options

Options for the FastTreeRankingFeaturizationEstimator.

FastTreeRankingModelParameters

Model parameters for FastTreeRankingTrainer.

FastTreeRankingTrainer

The IEstimator<TTransformer> for training a decision tree ranking model using FastTree.

FastTreeRankingTrainer.Options

Options for the FastTreeRankingTrainer as used in FastTree(Options).

FastTreeRegressionFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastTreeRegressionFeaturizationEstimator.Options

Options for the FastTreeRegressionFeaturizationEstimator.

FastTreeRegressionModelParameters

Model parameters for FastForestRegressionTrainer.

FastTreeRegressionTrainer

The IEstimator<TTransformer> for training a decision tree regression model using FastTree.

FastTreeRegressionTrainer.Options

Options for the FastTreeRegressionTrainer as used in FastTree(Options).

FastTreeTrainerBase<TOptions,TTransformer,TModel>
FastTreeTweedieFeaturizationEstimator

A IEstimator<TTransformer> to transform input feature vector to tree-based features.

FastTreeTweedieFeaturizationEstimator.Options

Options for the FastTreeTweedieFeaturizationEstimator.

FastTreeTweedieModelParameters

Model parameters for FastTreeTweedieTrainer.

FastTreeTweedieTrainer

The IEstimator<TTransformer> for training a decision tree regression model using Tweedie loss function. This trainer is a generalization of Poisson, compound Poisson, and gamma regression.

FastTreeTweedieTrainer.Options

Options for the FastTreeTweedieTrainer as used in FastTreeTweedie(Options).

GamBinaryModelParameters

Model parameters for GamBinaryTrainer.

GamBinaryTrainer

The IEstimator<TTransformer> for training a binary classification model with generalized additive models (GAM).

GamBinaryTrainer.Options

Options for the GamBinaryTrainer as used in Gam(Options).

GamModelParametersBase

The base class for GAM Model Parameters.

GamRegressionModelParameters

Model parameters for GamRegressionTrainer.

GamRegressionTrainer

The IEstimator<TTransformer> for training a regression model with generalized additive models (GAM).

GamRegressionTrainer.Options

Options for the GamRegressionTrainer as used in Gam(Options).

GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase

Base class for GAM-based trainer options.

GamTrainerBase<TOptions,TTransformer,TPredictor>

Base class for GAM trainers.

GeneralityLossRule

Loss of Generality (GL).

GeneralityToProgressRatioRule

Generality to Progress Ratio (PQ).

LowProgressRule

Low Progress (LP). This rule fires when the improvements on the score stall.

MovingWindowRule
PretrainedTreeFeaturizationEstimator

A IEstimator<TTransformer> which contains a pre-trained TreeEnsembleModelParameters and calling its Fit(IDataView) produces a featurizer based on the pre-trained model.

PretrainedTreeFeaturizationEstimator.Options

PretrainedTreeFeaturizationEstimator.Options of PretrainedTreeFeaturizationEstimator as used when calling FeaturizeByPretrainTreeEnsemble(TransformsCatalog, PretrainedTreeFeaturizationEstimator+Options).

QuantileRegressionTree

A container class for exposing Microsoft.ML.Trainers.FastTree.InternalQuantileRegressionTree's attributes to users. This class should not be mutable, so it contains a lot of read-only members. In addition to things inherited from RegressionTreeBase, we add GetLeafSamplesAt(Int32) and GetLeafSampleWeightsAt(Int32) to expose (sub-sampled) training labels falling into the leafIndex-th leaf and their weights.

QuantileRegressionTreeEnsemble
RandomForestTrainerBase<TOptions,TTransformer,TModel>
RegressionTree

A container class for exposing Microsoft.ML.Trainers.FastTree.InternalRegressionTree's attributes to users. This class should not be mutable, so it contains a lot of read-only members. Note that RegressionTree is identical to RegressionTreeBase but in another derived class QuantileRegressionTree some attributes are added.

RegressionTreeBase

A container base class for exposing Microsoft.ML.Trainers.FastTree.InternalRegressionTree's and Microsoft.ML.Trainers.FastTree.InternalQuantileRegressionTree's attributes to users. This class should not be mutable, so it contains a lot of read-only members.

RegressionTreeEnsemble
TolerantEarlyStoppingRule
TreeEnsemble<T>

A list of RegressionTreeBase's derived class. To compute the output value of a TreeEnsemble<T>, we need to compute the output values of all trees in Trees, scale those values via TreeWeights, and finally sum the scaled values and Bias up.

TreeEnsembleFeaturizationEstimatorBase

This class encapsulates the common behavior of all tree-based featurizers such as FastTreeBinaryFeaturizationEstimator, FastForestBinaryFeaturizationEstimator, FastTreeRegressionFeaturizationEstimator, FastForestRegressionFeaturizationEstimator, and PretrainedTreeFeaturizationEstimator. All tree-based featurizers share the same output schema computed by GetOutputSchema(SchemaShape). All tree-based featurizers requires an input feature column name and a suffix for all output columns. The ITransformer returned by Fit(IDataView) produces three columns: (1) the prediction values of all trees, (2) the IDs of leaves the input feature vector falling into, and (3) the binary vector which encodes the paths to those destination leaves.

TreeEnsembleFeaturizationEstimatorBase.OptionsBase

The common options of tree-based featurizations such as FastTreeBinaryFeaturizationEstimator, FastForestBinaryFeaturizationEstimator, FastTreeRegressionFeaturizationEstimator, FastForestRegressionFeaturizationEstimator, and PretrainedTreeFeaturizationEstimator.

TreeEnsembleFeaturizationTransformer

ITransformer resulting from fitting any derived class of TreeEnsembleFeaturizationEstimatorBase. The derived classes include, for example, FastTreeBinaryFeaturizationEstimator and FastForestRegressionFeaturizationEstimator.

TreeEnsembleModelParameters
TreeEnsembleModelParametersBasedOnQuantileRegressionTree

TreeEnsembleModelParametersBasedOnQuantileRegressionTree is derived from TreeEnsembleModelParameters plus a strongly-typed public attribute, TrainedTreeEnsemble, for exposing trained model's details to users. Its function, Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnQuantileRegressionTree.CreateTreeEnsembleFromInternalDataStructure, is called to create TrainedTreeEnsemble inside TreeEnsembleModelParameters. Note that the major difference between TreeEnsembleModelParametersBasedOnQuantileRegressionTree and TreeEnsembleModelParametersBasedOnRegressionTree is the type of TrainedTreeEnsemble.

TreeEnsembleModelParametersBasedOnRegressionTree

TreeEnsembleModelParametersBasedOnRegressionTree is derived from TreeEnsembleModelParameters plus a strongly-typed public attribute, TrainedTreeEnsemble, for exposing trained model's details to users. Its function, Microsoft.ML.Trainers.FastTree.TreeEnsembleModelParametersBasedOnRegressionTree.CreateTreeEnsembleFromInternalDataStructure, is called to create TrainedTreeEnsemble inside TreeEnsembleModelParameters. Note that the major difference between TreeEnsembleModelParametersBasedOnQuantileRegressionTree and TreeEnsembleModelParametersBasedOnRegressionTree is the type of TrainedTreeEnsemble.

TreeOptions

Options for tree trainers.

Enums

BoostedTreeOptions.OptimizationAlgorithmType

Types of optimization algorithms.

Bundle
EarlyStoppingMetric

Stopping measurements for classification and regression.

EarlyStoppingRankingMetric

Stopping measurements for ranking.