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

Definition

Namespace:

Microsoft.ML.Trainers.LightGbm

Assembly:

Microsoft.ML.LightGbm.dll

Package:

Microsoft.ML.LightGbm v3.0.1

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

Object

TrainerInputBase

TrainerInputBaseWithLabel

TrainerInputBaseWithWeight

TrainerInputBaseWithGroupId

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

Derived

Microsoft.ML.Trainers.LightGbm.LightGbmBinaryTrainer.Options

Microsoft.ML.Trainers.LightGbm.LightGbmMulticlassTrainer.Options

Microsoft.ML.Trainers.LightGbm.LightGbmRankingTrainer.Options

Microsoft.ML.Trainers.LightGbm.LightGbmRegressionTrainer.Options

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