File predictor_binary_example_wise.hpp¶
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namespace boosting
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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 regression scores or probability estimates to the label vectors encountered in the training data.
Subclassed by boosting::ExampleWiseBinaryPredictorConfig
Public Functions
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inline virtual ~IExampleWiseBinaryPredictorConfig()¶
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virtual bool isBasedOnProbabilities() const = 0¶
Returns whether binary predictions are derived from probability estimates rather than regression scores or not.
- Returns:
True, if binary predictions are derived from probability estimates rather than regression scores, false otherwise
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virtual IExampleWiseBinaryPredictorConfig &setBasedOnProbabilities(bool basedOnProbabilities) = 0¶
Sets whether binary predictions should be derived from probability estimates rather than regression scores or not.
- Parameters:
basedOnProbabilities – True, if binary predictions should be derived from probability estimates rather than regression scores, false otherwise
- Returns:
A reference to an object of type
IExampleWiseBinaryPredictorConfig
that allows further configuration of the predictor
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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
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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
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inline virtual ~IExampleWiseBinaryPredictorConfig()¶
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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 regression scores or probability estimates to the label vectors encountered in the training data.
Public Functions
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ExampleWiseBinaryPredictorConfig(const std::unique_ptr<ILossConfig> &lossConfigPtr, const std::unique_ptr<IMultiThreadingConfig> &multiThreadingConfigPtr)¶
- Parameters:
lossConfigPtr – A reference to an unique pointer that stores the configuration of the loss function
multiThreadingConfigPtr – A reference to an unique pointer that stores the configuration of the multi-threading behavior that should be used to predict for several query examples in parallel
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virtual bool isBasedOnProbabilities() const override¶
Returns whether binary predictions are derived from probability estimates rather than regression scores or not.
- Returns:
True, if binary predictions are derived from probability estimates rather than regression scores, false otherwise
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virtual IExampleWiseBinaryPredictorConfig &setBasedOnProbabilities(bool basedOnProbabilities) override¶
Sets whether binary predictions should be derived from probability estimates rather than regression scores or not.
- Parameters:
basedOnProbabilities – True, if binary predictions should be derived from probability estimates rather than regression scores, false otherwise
- Returns:
A reference to an object of type
IExampleWiseBinaryPredictorConfig
that allows further configuration of the predictor
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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
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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
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std::unique_ptr<IBinaryPredictorFactory> createPredictorFactory(const IRowWiseFeatureMatrix &featureMatrix, uint32 numLabels) const override¶
See also
IPredictorConfig::createPredictorFactory
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std::unique_ptr<ISparseBinaryPredictorFactory> createSparsePredictorFactory(const IRowWiseFeatureMatrix &featureMatrix, uint32 numLabels) const override¶
See also
IBinaryPredictorConfig::createSparsePredictorFactory
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bool isLabelVectorSetNeeded() const override¶
See also
IPredictorConfig::isLabelVectorSetNeeded
Private Members
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bool basedOnProbabilities_¶
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std::unique_ptr<IMarginalProbabilityCalibrationModel> noMarginalProbabilityCalibrationModelPtr_¶
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std::unique_ptr<IJointProbabilityCalibrationModel> noJointProbabilityCalibrationModelPtr_¶
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const std::unique_ptr<ILossConfig> &lossConfigPtr_¶
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const std::unique_ptr<IMultiThreadingConfig> &multiThreadingConfigPtr_¶
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ExampleWiseBinaryPredictorConfig(const std::unique_ptr<ILossConfig> &lossConfigPtr, const std::unique_ptr<IMultiThreadingConfig> &multiThreadingConfigPtr)¶
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class IExampleWiseBinaryPredictorConfig¶