FastForestRegressionTrainer.Options Class

Definition

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

public sealed class FastForestRegressionTrainer.Options : Microsoft.ML.Trainers.FastTree.FastForestOptionsBase
type FastForestRegressionTrainer.Options = class
    inherit FastForestOptionsBase
Public NotInheritable Class FastForestRegressionTrainer.Options
Inherits FastForestOptionsBase
Inheritance

Constructors

FastForestRegressionTrainer.Options()

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)
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)
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)
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)
HistogramPoolSize

The number of histograms in the pool (between 2 and numLeaves).

(Inherited from TreeOptions)
LabelColumnName

Column to use for labels.

(Inherited from TrainerInputBaseWithLabel)
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)
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)
NumberOfLeaves

The max number of leaves in each regression tree.

(Inherited from TreeOptions)
NumberOfQuantileSamples

The number of data points to be sampled from each leaf to find the distribution of labels.

(Inherited from FastForestOptionsBase)
NumberOfThreads

The number of threads to use.

(Inherited from TreeOptions)
NumberOfTrees

Total number of decision trees to create in the ensemble.

(Inherited from TreeOptions)
RowGroupColumnName

Column to use for example groupId.

(Inherited from TrainerInputBaseWithGroupId)
Seed

The seed of the random number generator.

(Inherited from TreeOptions)
ShuffleLabels

Whether to shuffle the labels on every iteration.

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)

Applies to