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LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase Class

Definition

public class LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase : Microsoft.ML.Trainers.TrainerInputBaseWithGroupId where TOptions : LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase, new() where TTransformer : ISingleFeaturePredictionTransformer<TModel> where TModel : class
type LightGbmTrainerBase<'Options, 'Output, 'ransformer, 'Model (requires 'Options :> LightGbmTrainerBase<'Options, 'Output, 'ransformer, 'Model>.OptionsBase and 'Options : (new : unit -> 'Options) and 'ransformer :> ISingleFeaturePredictionTransformer<'Model> and 'Model : null)>.OptionsBase = class
    inherit TrainerInputBaseWithGroupId
Public Class LightGbmTrainerBase(Of TOptions, TOutput, TTransformer, TModel).OptionsBase
Inherits TrainerInputBaseWithGroupId

Type Parameters

TOptions
TOutput
TTransformer
TModel
Inheritance
LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase
Derived

Fields

BatchSize

Number of data points per batch, when loading data.

CategoricalSmoothing

Laplace smooth term in categorical feature split. This can reduce the effect of noises in categorical features, especially for categories with few data.

EarlyStoppingRound

Determines the number of rounds, after which training will stop if validation metric doesn't improve.

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.

L2CategoricalRegularization

L2 regularization for categorical split.

LabelColumnName

Column to use for labels.

(Inherited from TrainerInputBaseWithLabel)
LearningRate

The shrinkage rate for trees, used to prevent over-fitting.

MaximumBinCountPerFeature

The maximum number of bins that feature values will be bucketed in.

MaximumCategoricalSplitPointCount

Maximum categorical split points to consider when splitting on a categorical feature.

MinimumExampleCountPerGroup

The minimum number of data points per categorical group.

MinimumExampleCountPerLeaf

The minimal number of data points required to form a new tree leaf.

NumberOfIterations

The number of boosting iterations. A new tree is created in each iteration, so this is equivalent to the number of trees.

NumberOfLeaves

The maximum number of leaves in one tree.

NumberOfThreads

Determines the number of threads used to run LightGBM.

RowGroupColumnName

Column to use for example groupId.

(Inherited from TrainerInputBaseWithGroupId)
Seed

The random seed for LightGBM to use.

Silent

Controls the logging level in LighGBM.

UseCategoricalSplit

Whether to enable categorical split or not.

UseZeroAsMissingValue

Whether to enable the usage of zero (0) as missing value.

Verbose

Determines whether to output progress status during training and evaluation.

Properties

Booster

Booster parameter to use

Applies to