LightGbmBinaryTrainer.Options Class

Definition

Namespace:

Microsoft.ML.Trainers.LightGbm

Assembly:

Microsoft.ML.LightGbm.dll

Package:

Microsoft.ML.LightGbm v3.0.1

public sealed class LightGbmBinaryTrainer.Options : Microsoft.ML.Trainers.LightGbm.LightGbmTrainerBase<Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.Options,float,Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LightGbm.LightGbmBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>,Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.LightGbm.LightGbmBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.OptionsBase

type LightGbmBinaryTrainer.Options = class
    inherit LightGbmTrainerBase<LightGbmBinaryTrainer.Options, single, BinaryPredictionTransformer<CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>.OptionsBase

Public NotInheritable Class LightGbmBinaryTrainer.Options
Inherits LightGbmTrainerBase(Of LightGbmBinaryTrainer.Options, Single, BinaryPredictionTransformer(Of CalibratedModelParametersBase(Of LightGbmBinaryModelParameters, PlattCalibrator)), CalibratedModelParametersBase(Of LightGbmBinaryModelParameters, PlattCalibrator)).OptionsBase

Inheritance

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

TrainerInputBaseWithGroupId

LightGbmTrainerBase<LightGbmBinaryTrainer.Options,Single,BinaryPredictionTransformer<CalibratedModelParametersBase<LightGbmBinaryModelParameters,PlattCalibrator>>,CalibratedModelParametersBase<LightGbmBinaryModelParameters,PlattCalibrator>>.OptionsBase

LightGbmBinaryTrainer.Options

Constructors

LightGbmBinaryTrainer.Options()

Fields

BatchSize	Number of data points per batch, when loading data. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
CategoricalSmoothing	Laplace smooth term in categorical feature split. This can reduce the effect of noises in categorical features, especially for categories with few data. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
EarlyStoppingRound	Determines the number of rounds, after which training will stop if validation metric doesn't improve. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
EvaluationMetric	Determines what evaluation metric to use.
ExampleWeightColumnName	Column to use for example weight. (Inherited from TrainerInputBaseWithWeight)
FeatureColumnName	Column to use for features. (Inherited from TrainerInputBase)
HandleMissingValue	Whether to enable special handling of missing value or not. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
L2CategoricalRegularization	L2 regularization for categorical split. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
LabelColumnName	Column to use for labels. (Inherited from TrainerInputBaseWithLabel)
LearningRate	The shrinkage rate for trees, used to prevent over-fitting. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
MaximumBinCountPerFeature	The maximum number of bins that feature values will be bucketed in. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
MaximumCategoricalSplitPointCount	Maximum categorical split points to consider when splitting on a categorical feature. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
MinimumExampleCountPerGroup	The minimum number of data points per categorical group. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
MinimumExampleCountPerLeaf	The minimal number of data points required to form a new tree leaf. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
NumberOfIterations	The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
NumberOfLeaves	The maximum number of leaves in one tree. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
NumberOfThreads	Determines the number of threads used to run LightGBM. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
RowGroupColumnName	Column to use for example groupId. (Inherited from TrainerInputBaseWithGroupId)
Seed	The random seed for LightGBM to use. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
Sigmoid	Parameter for the sigmoid function.
Silent	Controls the logging level in LighGBM. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
UnbalancedSets	Whether training data is unbalanced.
UseCategoricalSplit	Whether to enable categorical split or not. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
UseZeroAsMissingValue	Whether to enable the usage of zero (0) as missing value. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
Verbose	Determines whether to output progress status during training and evaluation. (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)
WeightOfPositiveExamples	Controls the balance of positive and negative weights in LightGbmBinaryTrainer.

Properties

Booster

Booster parameter to use (Inherited from LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase)