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LightGbmBinaryTrainer.Options Class

Definition

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

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)

Applies to