GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase Class

Definition

Namespace:

Microsoft.ML.Trainers.FastTree

Assembly:

Microsoft.ML.FastTree.dll

Package:

Microsoft.ML.FastTree v3.0.1

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

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase

Derived

Microsoft.ML.Trainers.FastTree.GamBinaryTrainer.Options

Microsoft.ML.Trainers.FastTree.GamRegressionTrainer.Options

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.