LightGbmTrainerBase<TOptions,TOutput,TTransformer,TModel>.OptionsBase Class
Definition
Important
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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 : classtype 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 TrainerInputBaseWithGroupIdPublic Class LightGbmTrainerBase(Of TOptions, TOutput, TTransformer, TModel).OptionsBase
Inherits TrainerInputBaseWithGroupIdType 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 |