# SdcaLogisticRegressionBinaryTrainer.Options Class

## Definition

public sealed class SdcaLogisticRegressionBinaryTrainer.Options : Microsoft.ML.Trainers.SdcaBinaryTrainerBase<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.BinaryOptionsBase
type SdcaLogisticRegressionBinaryTrainer.Options = class
inherit SdcaBinaryTrainerBase<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>.BinaryOptionsBase
Public NotInheritable Class SdcaLogisticRegressionBinaryTrainer.Options
Inherits SdcaBinaryTrainerBase(Of CalibratedModelParametersBase(Of LinearBinaryModelParameters, PlattCalibrator)).BinaryOptionsBase
Inheritance

## Fields

 The learning rate for adjusting bias from being regularized. (Inherited from SdcaTrainerBase.OptionsBase) Determines the frequency of checking for convergence in terms of number of iterations. (Inherited from SdcaTrainerBase.OptionsBase) The tolerance for the ratio between duality gap and primal loss for convergence checking. (Inherited from SdcaTrainerBase.OptionsBase) Column to use for example weight. (Inherited from TrainerInputBaseWithWeight) Column to use for features. (Inherited from TrainerInputBase) The L1 regularization hyperparameter. (Inherited from SdcaTrainerBase.OptionsBase) The L2 regularization hyperparameter. (Inherited from SdcaTrainerBase.OptionsBase) Column to use for labels. (Inherited from TrainerInputBaseWithLabel) The maximum number of passes to perform over the data. (Inherited from SdcaTrainerBase.OptionsBase) The degree of lock-free parallelism. (Inherited from SdcaTrainerBase.OptionsBase) The weight to be applied to the positive class. This is useful for training with imbalanced data. (Inherited from SdcaBinaryTrainerBase.BinaryOptionsBase) Determines whether to shuffle data for each training iteration. (Inherited from SdcaTrainerBase.OptionsBase)