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GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase Class

Definition

Base class for GAM-based trainer options.

public abstract class GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithWeight where TOptions : GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase, new() where TTransformer : ISingleFeaturePredictionTransformer<TPredictor> where TPredictor : class
type GamTrainerBase<'Options, 'ransformer, 'Predictor (requires 'Options :> GamTrainerBase<'Options, 'ransformer, 'Predictor>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Predictor> and 'Predictor : null)>.OptionsBase = class
    inherit TrainerInputBaseWithWeight
Public MustInherit Class GamTrainerBase(Of TOptions, TTransformer, TPredictor).OptionsBase
Inherits TrainerInputBaseWithWeight

Type Parameters

TOptions
TTransformer
TPredictor
Inheritance
GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase
Derived

Constructors

GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase()

Fields

DiskTranspose

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

EnablePruning

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

EntropyCoefficient

The entropy (regularization) coefficient between 0 and 1.

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.

GainConfidenceLevel

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

GetDerivativesSampleRate

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

LabelColumnName

Column to use for labels.

(Inherited from TrainerInputBaseWithLabel)
LearningRate

The learning rate.

MaximumBinCountPerFeature

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

MaximumTreeOutput

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

MinimumExampleCountPerLeaf

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

NumberOfIterations

Total number of passes over the training data.

NumberOfThreads

The number of threads to use.

Seed

The seed of the random number generator.

Applies to