SgdCalibratedTrainer.Options Class

Definition

Namespace:

Microsoft.ML.Trainers

Assembly:

Microsoft.ML.StandardTrainers.dll

Package:

Microsoft.ML v3.0.1

Options for the SgdCalibratedTrainer as used in SgdCalibrated(Options).

public sealed class SgdCalibratedTrainer.Options : Microsoft.ML.Trainers.SgdBinaryTrainerBase<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.OptionsBase

type SgdCalibratedTrainer.Options = class
    inherit SgdBinaryTrainerBase<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>.OptionsBase

Public NotInheritable Class SgdCalibratedTrainer.Options
Inherits SgdBinaryTrainerBase(Of CalibratedModelParametersBase(Of LinearBinaryModelParameters, PlattCalibrator)).OptionsBase

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

SgdBinaryTrainerBase<CalibratedModelParametersBase<LinearBinaryModelParameters,PlattCalibrator>>.OptionsBase

SgdCalibratedTrainer.Options

Constructors

SgdCalibratedTrainer.Options()

Fields

CheckFrequency	Determines the frequency of checking for convergence in terms of number of iterations. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
ConvergenceTolerance	The convergence tolerance. If the exponential moving average of loss reductions falls below this tolerance, the algorithm is deemed to have converged and will stop. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
L2Regularization	The L2 weight for regularization. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
LearningRate	The initial learning rate used by SGD. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
NumberOfIterations	The maximum number of passes through the training dataset. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
NumberOfThreads	The degree of lock-free parallelism used by SGD. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
PositiveInstanceWeight	The weight to be applied to the positive class. This is useful for training with imbalanced data. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)
Shuffle	Determines whether to shuffle data for each training iteration. (Inherited from SgdBinaryTrainerBase<TModel>.OptionsBase)