TreeOptions Class

Definition

Namespace:

Microsoft.ML.Trainers.FastTree

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v3.0.1

Options for tree trainers.

public abstract class TreeOptions : Microsoft.ML.Trainers.TrainerInputBaseWithGroupId

type TreeOptions = class
    inherit TrainerInputBaseWithGroupId

Public MustInherit Class TreeOptions
Inherits TrainerInputBaseWithGroupId

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

TrainerInputBaseWithGroupId

TreeOptions

Derived

Microsoft.ML.Trainers.FastTree.BoostedTreeOptions

Microsoft.ML.Trainers.FastTree.FastForestOptionsBase

Constructors

TreeOptions()

Fields

AllowEmptyTrees	When a root split is impossible, allow training to proceed.
BaggingExampleFraction	Percentage of training examples used in each bag. Default is 0.7 (70%).
BaggingSize	Number of trees in each bag (0 for disabling bagging).
Bias	Bias for calculating gradient for each feature bin for a categorical feature.
Bundling	Bundle low population bins. Bundle.None(0): no bundling, Bundle.AggregateLowPopulation(1): Bundle low population, Bundle.Adjacent(2): Neighbor low population bundle.
CategoricalSplit	Whether to do split based on multiple categorical feature values.
CompressEnsemble	Compress the tree Ensemble.
DiskTranspose	Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose.
EntropyCoefficient	The entropy (regularization) coefficient between 0 and 1.
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
ExecutionTime	Print execution time breakdown to ML.NET channel.
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
FeatureFirstUsePenalty	The feature first use penalty coefficient.
FeatureFlocks	Whether to collectivize features during dataset preparation to speed up training.
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.
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.
FeatureReusePenalty	The feature re-use penalty (regularization) coefficient.
FeatureSelectionSeed	The seed of the active feature selection.
GainConfidenceLevel	Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value.
HistogramPoolSize	The number of histograms in the pool (between 2 and numLeaves).
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
MaximumBinCountPerFeature	Maximum number of distinct values (bins) per feature.
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.
MaximumCategoricalSplitPointCount	Maximum categorical split points to consider when splitting on a categorical feature.
MemoryStatistics	Print memory statistics to ML.NET channel.
MinimumExampleCountPerLeaf	The minimal number of data points required to form a new tree leaf.
MinimumExampleFractionForCategoricalSplit	Minimum categorical example percentage in a bin to consider for a split. Default is 0.1% of all training examples.
MinimumExamplesForCategoricalSplit	Minimum categorical example count in a bin to consider for a split.
NumberOfLeaves	The max number of leaves in each regression tree.
NumberOfThreads	The number of threads to use.
NumberOfTrees	Total number of decision trees to create in the ensemble.
RowGroupColumnName	Column to use for example groupId. (Inherited from TrainerInputBaseWithGroupId)
Seed	The seed of the random number generator.
Smoothing	Smoothing parameter for tree regularization.
SoftmaxTemperature	The temperature of the randomized softmax distribution for choosing the feature.
SparsifyThreshold	Sparsity level needed to use sparse feature representation.
TestFrequency	Calculate metric values for train/valid/test every k rounds.