# SgdNonCalibratedTrainer.Options Class

## Definition

public sealed class SgdNonCalibratedTrainer.Options : Microsoft.ML.Trainers.SgdBinaryTrainerBase<Microsoft.ML.Trainers.LinearBinaryModelParameters>.OptionsBase
type SgdNonCalibratedTrainer.Options = class
inherit SgdBinaryTrainerBase<LinearBinaryModelParameters>.OptionsBase
Public NotInheritable Class SgdNonCalibratedTrainer.Options
Inherits SgdBinaryTrainerBase(Of LinearBinaryModelParameters).OptionsBase
Inheritance
SgdNonCalibratedTrainer.Options

## Fields

 Determines the frequency of checking for convergence in terms of number of iterations. (Inherited from SgdBinaryTrainerBase.OptionsBase) 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.OptionsBase) Column to use for example weight. (Inherited from TrainerInputBaseWithWeight) Column to use for features. (Inherited from TrainerInputBase) The L2 weight for regularization. (Inherited from SgdBinaryTrainerBase.OptionsBase) Column to use for labels. (Inherited from TrainerInputBaseWithLabel) The initial learning rate used by SGD. (Inherited from SgdBinaryTrainerBase.OptionsBase) The loss function to use. Default is LogLoss. The maximum number of passes through the training dataset. (Inherited from SgdBinaryTrainerBase.OptionsBase) The degree of lock-free parallelism used by SGD. (Inherited from SgdBinaryTrainerBase.OptionsBase) The weight to be applied to the positive class. This is useful for training with imbalanced data. (Inherited from SgdBinaryTrainerBase.OptionsBase) Determines whether to shuffle data for each training iteration. (Inherited from SgdBinaryTrainerBase.OptionsBase)