SdcaTrainerBase<TOptions,TTransformer,TModel>.OptionsBase Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

Options for the SDCA-based trainers.

public abstract class SdcaTrainerBase<TOptions,TTransformer,TModel>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithWeight where TOptions : SdcaTrainerBase<TOptions,TTransformer,TModel>.OptionsBase, new() where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class

type SdcaTrainerBase<'Options, 'ransformer, 'Model (requires 'Options :> SdcaTrainerBase<'Options, 'ransformer, 'Model>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>.OptionsBase = class
    inherit TrainerInputBaseWithWeight

Public MustInherit Class SdcaTrainerBase(Of TOptions, TTransformer, TModel).OptionsBase
Inherits TrainerInputBaseWithWeight

Type Parameters

TOptions

TTransformer

TModel

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

SdcaTrainerBase<TOptions,TTransformer,TModel>.OptionsBase

Derived

Microsoft.ML.Trainers.SdcaBinaryTrainerBase<TModelParameters>.BinaryOptionsBase

Microsoft.ML.Trainers.SdcaMulticlassTrainerBase<TModel>.MulticlassOptions

Microsoft.ML.Trainers.SdcaRegressionTrainer.Options

Fields

BiasLearningRate	The learning rate for adjusting bias from being regularized.
ConvergenceCheckFrequency	Determines the frequency of checking for convergence in terms of number of iterations.
ConvergenceTolerance	The tolerance for the ratio between duality gap and primal loss for convergence checking.
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
L1Regularization	The L1 regularization hyperparameter.
L2Regularization	The L2 regularization hyperparameter.
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
MaximumNumberOfIterations	The maximum number of passes to perform over the data.
NumberOfThreads	The degree of lock-free parallelism.
Shuffle	Determines whether to shuffle data for each training iteration.