Share via


GamBinaryTrainer.Options Class

Definition

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

public sealed class GamBinaryTrainer.Options : Microsoft.ML.Trainers.FastTree.GamTrainerBase<Microsoft.ML.Trainers.FastTree.GamBinaryTrainer.Options,Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.GamBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>,Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.GamBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.OptionsBase
type GamBinaryTrainer.Options = class
    inherit GamTrainerBase<GamBinaryTrainer.Options, BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>.OptionsBase
Public NotInheritable Class GamBinaryTrainer.Options
Inherits GamTrainerBase(Of GamBinaryTrainer.Options, BinaryPredictionTransformer(Of CalibratedModelParametersBase(Of GamBinaryModelParameters, PlattCalibrator)), CalibratedModelParametersBase(Of GamBinaryModelParameters, PlattCalibrator)).OptionsBase
Inheritance

Constructors

GamBinaryTrainer.Options()

Fields

DiskTranspose

Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
EnablePruning

Enable post-training tree pruning to avoid overfitting. It requires a validation set.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
EntropyCoefficient

The entropy (regularization) coefficient between 0 and 1.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
ExampleWeightColumnName

Column to use for example weight.

(Inherited from TrainerInputBaseWithWeight)
FeatureColumnName

Column to use for features.

(Inherited from TrainerInputBase)
FeatureFlocks

Whether to collectivize features during dataset preparation to speed up training.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
GainConfidenceLevel

Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
GetDerivativesSampleRate

Sample each query 1 in k times in the GetDerivatives function.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
LabelColumnName

Column to use for labels.

(Inherited from TrainerInputBaseWithLabel)
LearningRate

The learning rate.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
MaximumBinCountPerFeature

The maximum number of distinct values (bins) per feature.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
MaximumTreeOutput

The upper bound on the absolute value of a single tree output.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
MinimumExampleCountPerLeaf

The minimal number of data points required to form a new tree leaf.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
NumberOfIterations

Total number of passes over the training data.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
NumberOfThreads

The number of threads to use.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
Seed

The seed of the random number generator.

(Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase)
UnbalancedSets

Whether to use derivatives optimized for unbalanced training data.

Applies to