GamBinaryTrainer.Options Class
Definition
Important
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Options for the GamBinaryTrainer as used in Gam(Options).
public sealed class GamBinaryTrainer.Options : Microsoft.ML.Trainers.FastTree.GamTrainerBase<Microsoft.ML.Trainers.FastTree.GamBinaryTrainer.Options,Microsoft.ML.Data.BinaryPredictionTransformer<Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.GamBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>,Microsoft.ML.Calibrators.CalibratedModelParametersBase<Microsoft.ML.Trainers.FastTree.GamBinaryModelParameters,Microsoft.ML.Calibrators.PlattCalibrator>>.OptionsBasetype GamBinaryTrainer.Options = class
inherit GamTrainerBase<GamBinaryTrainer.Options, BinaryPredictionTransformer<CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>, CalibratedModelParametersBase<GamBinaryModelParameters, PlattCalibrator>>.OptionsBasePublic NotInheritable Class GamBinaryTrainer.Options
Inherits GamTrainerBase(Of GamBinaryTrainer.Options, BinaryPredictionTransformer(Of CalibratedModelParametersBase(Of GamBinaryModelParameters, PlattCalibrator)), CalibratedModelParametersBase(Of GamBinaryModelParameters, PlattCalibrator)).OptionsBase- Inheritance
Constructors
| GamBinaryTrainer.Options() |
Fields
| DiskTranspose |
Whether to utilize the disk or the data's native transposition facilities (where applicable) when performing the transpose. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| EnablePruning |
Enable post-training tree pruning to avoid overfitting. It requires a validation set. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| EntropyCoefficient |
The entropy (regularization) coefficient between 0 and 1. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| ExampleWeightColumnName |
Column to use for example weight. (Inherited from TrainerInputBaseWithWeight) |
| FeatureColumnName |
Column to use for features. (Inherited from TrainerInputBase) |
| FeatureFlocks |
Whether to collectivize features during dataset preparation to speed up training. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| GainConfidenceLevel |
Tree fitting gain confidence requirement. Only consider a gain if its likelihood versus a random choice gain is above this value. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| GetDerivativesSampleRate |
Sample each query 1 in k times in the GetDerivatives function. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| LabelColumnName |
Column to use for labels. (Inherited from TrainerInputBaseWithLabel) |
| LearningRate |
The learning rate. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| MaximumBinCountPerFeature |
The maximum number of distinct values (bins) per feature. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| MaximumTreeOutput |
The upper bound on the absolute value of a single tree output. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| MinimumExampleCountPerLeaf |
The minimal number of data points required to form a new tree leaf. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| NumberOfIterations |
Total number of passes over the training data. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| NumberOfThreads |
The number of threads to use. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| Seed |
The seed of the random number generator. (Inherited from GamTrainerBase<TOptions,TTransformer,TPredictor>.OptionsBase) |
| UnbalancedSets |
Whether to use derivatives optimized for unbalanced training data. |