File learner_classification.hpp¶

namespace boosting

class INonDecomposableLogisticLossMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a loss function that implements a multivariate variant of the logistic loss that is non-decomposable.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~INonDecomposableLogisticLossMixin() override¶

inline virtual void useNonDecomposableLogisticLoss()¶: Configures the rule learner to use a loss function that implements a multivariate variant of the logistic loss that is non-decomposable.

class INonDecomposableSquaredHingeLossMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is non-decomposable.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~INonDecomposableSquaredHingeLossMixin() override¶

inline virtual void useNonDecomposableSquaredHingeLoss()¶: Configures the rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is non-decomposable.

class IDecomposableLogisticLossMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a loss function that implements a multivariate variant of the logistic loss that is decomposable.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IDecomposableLogisticLossMixin() override¶

inline virtual void useDecomposableLogisticLoss()¶: Configures the rule learner to use a loss function that implements a multivariate variant of the logistic loss that is applied decomposable.

class IDecomposableSquaredHingeLossMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is decomposable.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IDecomposableSquaredHingeLossMixin() override¶

inline virtual void useDecomposableSquaredHingeLoss()¶: Configures the rule learner to use a loss function that implements a multivariate variant of the squared hinge loss that is decomposable.

class IEqualWidthLabelBinningMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a method for the assignment of labels to bins.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IEqualWidthLabelBinningMixin() override¶

inline virtual IEqualWidthLabelBinningConfig &useEqualWidthLabelBinning()¶

Configures the rule learner to use a method for the assignment of labels to bins in a way such that each bin contains labels for which the predicted score is expected to belong to the same value range.

Returns:: A reference to an object of type IEqualWidthLabelBinningConfig that allows further configuration of the method for the assignment of labels to bins

class IAutomaticLabelBinningMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to automatically decide whether a method for the assignment of labels to bins should be used or not.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IAutomaticLabelBinningMixin() override¶

inline virtual void useAutomaticLabelBinning()¶: Configures the rule learner to automatically decide whether a method for the assignment of labels to bins should be used or not.

class IIsotonicMarginalProbabilityCalibrationMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to calibrate marginal probabilities via isotonic regression.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IIsotonicMarginalProbabilityCalibrationMixin() override¶

inline virtual IIsotonicMarginalProbabilityCalibratorConfig &useIsotonicMarginalProbabilityCalibration()¶

Configures the rule learner to calibrate marginal probabilities via isotonic regression.

Returns:: A reference to an object of type IIsotonicMarginalProbabilityCalibratorConfig that allows further configuration of the calibrator

class IIsotonicJointProbabilityCalibrationMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to calibrate joint probabilities via isotonic regression.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IIsotonicJointProbabilityCalibrationMixin() override¶

inline virtual IIsotonicJointProbabilityCalibratorConfig &useIsotonicJointProbabilityCalibration()¶

Configures the rule learner to calibrate joint probabilities via isotonic regression.

Returns:: A reference to an object of type IIsotonicJointProbabilityCalibratorConfig that allows further configuration of the calibrator

class IOutputWiseProbabilityPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a predictor that predicts label-wise probabilities for given query examples by transforming the individual scores that are predicted for each label into probabilities.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IOutputWiseProbabilityPredictorMixin() override¶

inline virtual IOutputWiseProbabilityPredictorConfig &useOutputWiseProbabilityPredictor()¶

Configures the rule learner to use a predictor that predicts label-wise probabilities for given query examples by transforming the individual scores that are predicted for each label into probabilities.

Returns:: A reference to an object of type IOutputWiseProbabilityPredictorConfig that allows further configuration of the predictor

class IMarginalizedProbabilityPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use predictor that predicts label-wise probabilities for given query examples by marginalizing over the joint probabilities of known label vectors.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IMarginalizedProbabilityPredictorMixin() override¶

inline virtual IMarginalizedProbabilityPredictorConfig &useMarginalizedProbabilityPredictor()¶

Configures the rule learner to use a predictor that predicts label-wise probabilities for given query examples by marginalizing over the joint probabilities of known label vectors.

Returns:: A reference to an object of type IMarginalizedProbabilityPredictorConfig that allows further configuration of the predictor

class IAutomaticProbabilityPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to automatically decide for a predictor for predicting probability estimates.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IAutomaticProbabilityPredictorMixin() override¶

inline virtual void useAutomaticProbabilityPredictor()¶: Configures the rule learner to automatically decide for a predictor for predicting probability estimates.

class IOutputWiseBinaryPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the individual scores or probability estimates that are predicted for each label.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IOutputWiseBinaryPredictorMixin() override¶

inline virtual IOutputWiseBinaryPredictorConfig &useOutputWiseBinaryPredictor()¶

Configures the rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the individual scores or probability estimates that are predicted for each label.

Returns:: A reference to an object of type IOutputWiseBinaryPredictorConfig that allows further configuration of the predictor

class IExampleWiseBinaryPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use 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::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IExampleWiseBinaryPredictorMixin() override¶

inline virtual IExampleWiseBinaryPredictorConfig &useExampleWiseBinaryPredictor()¶

Configures the rule learner to use 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.

Returns:: A reference to an object of type IExampleWiseBinaryPredictorConfig that allows further configuration of the predictor

class IGfmBinaryPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IGfmBinaryPredictorMixin() override¶

inline virtual IGfmBinaryPredictorConfig &useGfmBinaryPredictor()¶

Configures the rule learner to use a predictor that predicts whether individual labels of given query examples are relevant or irrelevant by discretizing the scores or probability estimates that are predicted for each label according to the general F-measure maximizer (GFM).

Returns:: A reference to an object of type IGfmBinaryPredictorConfig that allows further configuration of the predictor

class IAutomaticBinaryPredictorMixin : public virtual boosting::IBoostedRuleLearnerConfig ¶

#include <learner_classification.hpp>

Defines an interface for all classes that allow to configure a rule learner to automatically decide for a predictor for predicting whether individual labels are relevant or irrelevant.

Subclassed by boosting::IBoomerClassifier::IConfig

Public Functions

inline virtual ~IAutomaticBinaryPredictorMixin() override¶

inline virtual void useAutomaticBinaryPredictor()¶: Configures the rule learner to automatically decide for a predictor for predicting whether individual labels are relevant or irrelevant.