File predictor_binary_example_wise.hpp¶

namespace boosting

class IExampleWiseBinaryPredictorConfig¶

#include <predictor_binary_example_wise.hpp>

Defines an interface for all classes that allow to configure a predictor that predicts known label vectors for given query examples by comparing the predicted scores or probability estimates to the label vectors encountered in the training data.

Subclassed by boosting::ExampleWiseBinaryPredictorConfig

Public Functions

inline virtual ~IExampleWiseBinaryPredictorConfig()¶

virtual bool isBasedOnProbabilities() const = 0¶

Returns whether binary predictions are derived from probability estimates rather than scores or not.

Returns:: True, if binary predictions are derived from probability estimates rather than scores, false otherwise

virtual IExampleWiseBinaryPredictorConfig &setBasedOnProbabilities(bool basedOnProbabilities) = 0¶

Sets whether binary predictions should be derived from probability estimates rather than scores or not.

Parameters:: basedOnProbabilities – True, if binary predictions should be derived from probability estimates rather than scores, false otherwise
Returns:: A reference to an object of type IExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

virtual bool isProbabilityCalibrationModelUsed() const = 0¶

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:: True, if a model for the calibration of probabilities is used, if available, false otherwise

virtual IExampleWiseBinaryPredictorConfig &setUseProbabilityCalibrationModel(bool useProbabilityCalibrationModel) = 0¶

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:: useProbabilityCalibrationModel – True, if a model for the calibration of probabilities should be used, if available, false otherwise
Returns:: A reference to an object of type IExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

class ExampleWiseBinaryPredictorConfig : public boosting::IExampleWiseBinaryPredictorConfig, public IBinaryPredictorConfig ¶

#include <predictor_binary_example_wise.hpp>

Allows to configure a predictor that predicts known label vectors for given query examples by comparing the predicted scores or probability estimates to the label vectors encountered in the training data.

Public Functions

ExampleWiseBinaryPredictorConfig(ReadableProperty<IClassificationLossConfig> lossConfig, ReadableProperty<IMultiThreadingConfig> multiThreadingConfig)¶

Parameters:

lossConfig – A ReadableProperty that allows to access the IClassificationLossConfig that stores the configuration of the loss function
multiThreadingConfig – A ReadableProperty that allows to access the IMultiThreadingConfig that stores the configuration of the multi-threading behavior that should be used to predict for several query examples in parallel

virtual bool isBasedOnProbabilities() const override¶

Returns whether binary predictions are derived from probability estimates rather than scores or not.

Returns:: True, if binary predictions are derived from probability estimates rather than scores, false otherwise

virtual IExampleWiseBinaryPredictorConfig &setBasedOnProbabilities(bool basedOnProbabilities) override¶

Sets whether binary predictions should be derived from probability estimates rather than scores or not.

Parameters:: basedOnProbabilities – True, if binary predictions should be derived from probability estimates rather than scores, false otherwise
Returns:: A reference to an object of type IExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

virtual bool isProbabilityCalibrationModelUsed() const override¶

Returns whether a model for the calibration of probabilities is used, if available, or not.

Returns:: True, if a model for the calibration of probabilities is used, if available, false otherwise

virtual IExampleWiseBinaryPredictorConfig &setUseProbabilityCalibrationModel(bool useProbabilityCalibrationModel) override¶

Sets whether a model for the calibration of probabilities should be used, if available, or not.

Parameters:: useProbabilityCalibrationModel – True, if a model for the calibration of probabilities should be used, if available, false otherwise
Returns:: A reference to an object of type IExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

std::unique_ptr<IBinaryPredictorFactory> createPredictorFactory(const IRowWiseFeatureMatrix &featureMatrix, uint32 numOutputs) const override¶