GamBinaryTrainer.Options Class

Definition

Namespace:

Microsoft.ML.Trainers.FastTree

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v3.0.1

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

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

GamTrainerBase<GamBinaryTrainer.Options,BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters,PlattCalibrator>>,CalibratedModelParametersBase<GamBinaryModelParameters,PlattCalibrator>>.OptionsBase

GamBinaryTrainer.Options

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.