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BoostedTreeOptions
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Options for boosting tree trainers.
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BoostingFastTreeTrainerBase<TOptions,TTransformer,TModel>
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ConsecutiveGeneralityLossRule
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Consecutive Loss in Generality (UP).
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EarlyStoppingRule
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EarlyStoppingRuleBase
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Early stopping rule used to terminate training process once meeting a specified criterion.
Used for setting EarlyStoppingRuleEarlyStoppingRule.
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FastForestBinaryFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastForestBinaryFeaturizationEstimator.Options
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Options for the FastForestBinaryFeaturizationEstimator.
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FastForestBinaryModelParameters
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Model parameters for FastForestBinaryTrainer.
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FastForestBinaryTrainer
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The IEstimator<TTransformer> for training a decision tree binary classification model using Fast Forest.
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FastForestBinaryTrainer.Options
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Options for the FastForestBinaryTrainer as used in
FastForest(Options).
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FastForestOptionsBase
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Base class for fast forest trainer options.
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FastForestRegressionFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastForestRegressionFeaturizationEstimator.Options
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Options for the FastForestRegressionFeaturizationEstimator.
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FastForestRegressionModelParameters
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Model parameters for FastForestRegressionTrainer.
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FastForestRegressionTrainer
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The IEstimator<TTransformer> for training a decision tree regression model using Fast Forest.
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FastForestRegressionTrainer.Options
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Options for the FastForestRegressionTrainer as used in
FastForest(Options).
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FastTreeBinaryFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastTreeBinaryFeaturizationEstimator.Options
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Options for the FastTreeBinaryFeaturizationEstimator.
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FastTreeBinaryModelParameters
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Model parameters for FastTreeBinaryTrainer.
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FastTreeBinaryTrainer
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The IEstimator<TTransformer> for training a decision tree binary classification model using FastTree.
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FastTreeBinaryTrainer.Options
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Options for the FastTreeBinaryTrainer as used in
FastTree(Options).
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FastTreeRankingFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastTreeRankingFeaturizationEstimator.Options
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Options for the FastTreeRankingFeaturizationEstimator.
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FastTreeRankingModelParameters
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Model parameters for FastTreeRankingTrainer.
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FastTreeRankingTrainer
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The IEstimator<TTransformer> for training a decision tree ranking model using FastTree.
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FastTreeRankingTrainer.Options
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Options for the FastTreeRankingTrainer as used in
FastTree(Options).
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FastTreeRegressionFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastTreeRegressionFeaturizationEstimator.Options
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Options for the FastTreeRegressionFeaturizationEstimator.
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FastTreeRegressionModelParameters
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Model parameters for FastForestRegressionTrainer.
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FastTreeRegressionTrainer
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The IEstimator<TTransformer> for training a decision tree regression model using FastTree.
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FastTreeRegressionTrainer.Options
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Options for the FastTreeRegressionTrainer as used in
FastTree(Options).
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FastTreeTrainerBase<TOptions,TTransformer,TModel>
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FastTreeTweedieFeaturizationEstimator
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A IEstimator<TTransformer> to transform input feature vector to tree-based features.
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FastTreeTweedieFeaturizationEstimator.Options
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Options for the FastTreeTweedieFeaturizationEstimator.
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FastTreeTweedieModelParameters
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Model parameters for FastTreeTweedieTrainer.
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FastTreeTweedieTrainer
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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.
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FastTreeTweedieTrainer.Options
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Options for the FastTreeTweedieTrainer as used in
FastTreeTweedie(Options).
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GamBinaryModelParameters
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Model parameters for GamBinaryTrainer.
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GamBinaryTrainer
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The IEstimator<TTransformer> for training a binary classification model with generalized additive models (GAM).
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GamBinaryTrainer.Options
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Options for the GamBinaryTrainer as used in
Gam(Options).
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GamModelParametersBase
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The base class for GAM Model Parameters.
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GamRegressionModelParameters
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Model parameters for GamRegressionTrainer.
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GamRegressionTrainer
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The IEstimator<TTransformer> for training a regression model with generalized additive models (GAM).
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GamRegressionTrainer.Options
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Options for the GamRegressionTrainer as used in
Gam(Options).
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GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase
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Base class for GAM-based trainer options.
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GamTrainerBase<TOptions,TTransformer,TPredictor>
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Base class for GAM trainers.
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GeneralityLossRule
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Loss of Generality (GL).
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GeneralityToProgressRatioRule
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Generality to Progress Ratio (PQ).
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LowProgressRule
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Low Progress (LP).
This rule fires when the improvements on the score stall.
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MovingWindowRule
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PretrainedTreeFeaturizationEstimator
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A IEstimator<TTransformer> which contains a pre-trained TreeEnsembleModelParameters and calling its
Fit(IDataView) produces a featurizer based on the pre-trained model.
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PretrainedTreeFeaturizationEstimator.Options
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PretrainedTreeFeaturizationEstimator.Options of PretrainedTreeFeaturizationEstimator as
used when calling FeaturizeByPretrainTreeEnsemble(TransformsCatalog, PretrainedTreeFeaturizationEstimator+Options).
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QuantileRegressionTree
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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.
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QuantileRegressionTreeEnsemble
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RandomForestTrainerBase<TOptions,TTransformer,TModel>
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RegressionTree
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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.
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RegressionTreeBase
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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.
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RegressionTreeEnsemble
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TolerantEarlyStoppingRule
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TreeEnsemble<T>
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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.
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TreeEnsembleFeaturizationEstimatorBase
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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.
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TreeEnsembleFeaturizationEstimatorBase.OptionsBase
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The common options of tree-based featurizations such as FastTreeBinaryFeaturizationEstimator, FastForestBinaryFeaturizationEstimator,
FastTreeRegressionFeaturizationEstimator, FastForestRegressionFeaturizationEstimator, and PretrainedTreeFeaturizationEstimator.
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TreeEnsembleFeaturizationTransformer
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ITransformer resulting from fitting any derived class of TreeEnsembleFeaturizationEstimatorBase.
The derived classes include, for example, FastTreeBinaryFeaturizationEstimator and
FastForestRegressionFeaturizationEstimator.
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TreeEnsembleModelParameters
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TreeEnsembleModelParametersBasedOnQuantileRegressionTree
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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.
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TreeEnsembleModelParametersBasedOnRegressionTree
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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.
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TreeOptions
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Options for tree trainers.
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