LightGbmBinaryTrainer.Options Class
Definition
Important
Some information relates to prerelease product that may be substantially modified before it’s released. Microsoft makes no warranties, express or implied, with respect to the information provided here.
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>>.OptionsBasetype LightGbmBinaryTrainer.Options = class
inherit LightGbmTrainerBase<LightGbmBinaryTrainer.Options, single, BinaryPredictionTransformer<CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<LightGbmBinaryModelParameters, PlattCalibrator>>.OptionsBasePublic 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) |