# LbfgsLogisticRegressionBinaryTrainer.Options Class

## Definition

public sealed class LbfgsLogisticRegressionBinaryTrainer.Options : Microsoft.ML.Trainers.LbfgsTrainerBase<Microsoft.ML.Trainers.LbfgsLogisticRegressionBinaryTrainer.Options,Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>,Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LinearBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.OptionsBase
type LbfgsLogisticRegressionBinaryTrainer.Options = class
inherit LbfgsTrainerBase<LbfgsLogisticRegressionBinaryTrainer.Options, BinaryPredictionTransformer<CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<LinearBinaryModelParameters, PlattCalibrator>>.OptionsBase
Public NotInheritable Class LbfgsLogisticRegressionBinaryTrainer.Options
Inherits LbfgsTrainerBase(Of LbfgsLogisticRegressionBinaryTrainer.Options, BinaryPredictionTransformer(Of CalibratedModelParametersBase(Of LinearBinaryModelParameters, PlattCalibrator)), CalibratedModelParametersBase(Of LinearBinaryModelParameters, PlattCalibrator)).OptionsBase
Inheritance

## Fields

 The instance of ComputeLogisticRegressionStandardDeviation that computes the std of the training statistics, at the end of training. The calculations are not part of Microsoft.ML package, due to the size of MKL. If you need these calculations, add the Microsoft.ML.Mkl.Components package, and initialize ComputeStandardDeviation. to the ComputeLogisticRegressionStandardDeviation implementation in the Microsoft.ML.Mkl.Components package. Force densification of the internal optimization vectors. Default is false. (Inherited from LbfgsTrainerBase.OptionsBase) Enforce non-negative weights. Default is false. (Inherited from LbfgsTrainerBase.OptionsBase) Column to use for example weight. (Inherited from TrainerInputBaseWithWeight) Column to use for features. (Inherited from TrainerInputBase) Number of previous iterations to remember for estimating the Hessian. Lower values mean faster but less accurate estimates. (Inherited from LbfgsTrainerBase.OptionsBase) Initial weights scale. (Inherited from LbfgsTrainerBase.OptionsBase) L1 regularization weight. (Inherited from LbfgsTrainerBase.OptionsBase) L2 regularization weight. (Inherited from LbfgsTrainerBase.OptionsBase) Column to use for labels. (Inherited from TrainerInputBaseWithLabel) Number of iterations. (Inherited from LbfgsTrainerBase.OptionsBase) Number of threads. Null means use the number of processors. (Inherited from LbfgsTrainerBase.OptionsBase) Tolerance parameter for optimization convergence. (Low = slower, more accurate). (Inherited from LbfgsTrainerBase.OptionsBase) Determines whether to produce output during training or not. (Inherited from LbfgsTrainerBase.OptionsBase) If set to true training statistics will be generated at the end of training. If you have a large number of learned training parameters(more than 500), generating the training statistics might take a few seconds. More than 1000 weights might take a few minutes. For those cases consider using the instance of ComputeLogisticRegressionStandardDeviation present in the Microsoft.ML.Mkl.Components package. That computes the statistics using hardware acceleration. Run SGD to initialize LR weights, converging to this tolerance. (Inherited from LbfgsTrainerBase.OptionsBase)