BoostedTreeOptions Class

Definition

Namespace:

Microsoft.ML.Trainers.FastTree

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v3.0.1

Options for boosting tree trainers.

public abstract class BoostedTreeOptions : Microsoft.ML.Trainers.FastTree.TreeOptions

type BoostedTreeOptions = class
    inherit TreeOptions

Public MustInherit Class BoostedTreeOptions
Inherits TreeOptions

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

TrainerInputBaseWithGroupId

TreeOptions

BoostedTreeOptions

Derived

Microsoft.ML.Trainers.FastTree.FastTreeBinaryTrainer.Options

Microsoft.ML.Trainers.FastTree.FastTreeRankingTrainer.Options

Microsoft.ML.Trainers.FastTree.FastTreeRegressionTrainer.Options

Microsoft.ML.Trainers.FastTree.FastTreeTweedieTrainer.Options

Constructors

BoostedTreeOptions()

Fields

AllowEmptyTrees	When a root split is impossible, allow training to proceed. (Inherited from TreeOptions)
BaggingExampleFraction	Percentage of training examples used in each bag. Default is 0.7 (70%). (Inherited from TreeOptions)
BaggingSize	Number of trees in each bag (0 for disabling bagging). (Inherited from TreeOptions)
BestStepRankingRegressionTrees	Option for using best regression step trees.
Bias	Bias for calculating gradient for each feature bin for a categorical feature. (Inherited from TreeOptions)
Bundling	Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle. (Inherited from TreeOptions)
CategoricalSplit	Whether to do split based on multiple categorical feature values. (Inherited from TreeOptions)
CompressEnsemble	Compress the tree Ensemble. (Inherited from TreeOptions)
DiskTranspose	Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose. (Inherited from TreeOptions)
DropoutRate	Dropout rate for tree regularization.
EnablePruning	Enable post-training tree pruning to avoid overfitting. It requires a validation set.
EntropyCoefficient	The entropy (regularization) coefficient between 0 and 1. (Inherited from TreeOptions)
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
ExecutionTime	Print execution time breakdown to ML.NET channel. (Inherited from TreeOptions)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
FeatureFirstUsePenalty	The feature first use penalty coefficient. (Inherited from TreeOptions)
FeatureFlocks	Whether to collectivize features during dataset preparation to speed up training. (Inherited from TreeOptions)
FeatureFraction	The fraction of features (chosen randomly) to use on each iteration. Use 0.9 if only 90% of features is needed. Lower numbers help reduce over-fitting. (Inherited from TreeOptions)
FeatureFractionPerSplit	The fraction of features (chosen randomly) to use on each split. If it's value is 0.9, 90% of all features would be dropped in expectation. (Inherited from TreeOptions)
FeatureReusePenalty	The feature re-use penalty (regularization) coefficient. (Inherited from TreeOptions)
FeatureSelectionSeed	The seed of the active feature selection. (Inherited from TreeOptions)
FilterZeroLambdas	Filter zero lambdas during training.
GainConfidenceLevel	Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value. (Inherited from TreeOptions)
GetDerivativesSampleRate	Sample each query 1 in k times in the GetDerivatives function.
HistogramPoolSize	The number of histograms in the pool (between 2 and numLeaves). (Inherited from TreeOptions)
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
LearningRate	The learning rate.
MaximumBinCountPerFeature	Maximum number of distinct values (bins) per feature. (Inherited from TreeOptions)
MaximumCategoricalGroupCountPerNode	Maximum categorical split groups to consider when splitting on a categorical feature. Split groups are a collection of split points. This is used to reduce overfitting when there many categorical features. (Inherited from TreeOptions)
MaximumCategoricalSplitPointCount	Maximum categorical split points to consider when splitting on a categorical feature. (Inherited from TreeOptions)
MaximumNumberOfLineSearchSteps	Number of post-bracket line search steps.
MaximumTreeOutput	Upper bound on absolute value of single tree output.
MemoryStatistics	Print memory statistics to ML.NET channel. (Inherited from TreeOptions)
MinimumExampleCountPerLeaf	The minimal number of data points required to form a new tree leaf. (Inherited from TreeOptions)
MinimumExampleFractionForCategoricalSplit	Minimum categorical example percentage in a bin to consider for a split. Default is 0.1% of all training examples. (Inherited from TreeOptions)
MinimumExamplesForCategoricalSplit	Minimum categorical example count in a bin to consider for a split. (Inherited from TreeOptions)
MinimumStepSize	Minimum line search step size.
NumberOfLeaves	The max number of leaves in each regression tree. (Inherited from TreeOptions)
NumberOfThreads	The number of threads to use. (Inherited from TreeOptions)
NumberOfTrees	Total number of decision trees to create in the ensemble. (Inherited from TreeOptions)
OptimizationAlgorithm	Optimization algorithm to be used.
PruningThreshold	The tolerance threshold for pruning.
PruningWindowSize	The moving window size for pruning.
RandomStart	Training starts from random ordering (determined by /r1).
RowGroupColumnName	Column to use for example groupId. (Inherited from TrainerInputBaseWithGroupId)
Seed	The seed of the random number generator. (Inherited from TreeOptions)
Shrinkage	Shrinkage.
Smoothing	Smoothing parameter for tree regularization. (Inherited from TreeOptions)
SoftmaxTemperature	The temperature of the randomized softmax distribution for choosing the feature. (Inherited from TreeOptions)
SparsifyThreshold	Sparsity level needed to use sparse feature representation. (Inherited from TreeOptions)
TestFrequency	Calculate metric values for train/valid/test every k rounds. (Inherited from TreeOptions)
UseLineSearch	Determines whether to use line search for a step size.
UseTolerantPruning	Use window and tolerance for pruning.
WriteLastEnsemble	Write the last ensemble instead of the one determined by early stopping.

Properties

EarlyStoppingRule

Early stopping rule used to terminate training process once meeting a specified criterion. Possible choices are EarlyStoppingRuleBase's implementations such as TolerantEarlyStoppingRule and GeneralityLossRule.