slope::MisClass Class Reference

Misclassification Rate scoring metric. More...

#include <score.h>

Inheritance diagram for slope::MisClass:

Collaboration diagram for slope::MisClass:

Public Member Functions
double	eval (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const std::unique_ptr< Loss > &loss) const override
double	eval (const Eigen::MatrixXd &eta, const Eigen::MatrixXd &y, const std::unique_ptr< Loss > &loss, const Eigen::VectorXd &observation_weights) const override
Public Member Functions inherited from slope::MinimizeScore
bool	isWorse (double a, double b) const override
double	initValue () const override
Public Member Functions inherited from slope::Score
std::function< bool(double, double)>	getComparator () const

Additional Inherited Members
Static Public Member Functions inherited from slope::Score
static std::unique_ptr< Score >	create (const std::string &metric)

Detailed Description

Misclassification Rate scoring metric.

Computes the proportion of incorrect predictions in classification problems. Inherits from MinimizeScore since lower misclassification rates indicate better performance.

Misclassification Rate = 1 - Accuracy = (number of incorrect predictions) / (total number of predictions)

For binary classification, predictions are thresholded at 0.5. For multi-class problems, the class with highest probability is selected as the prediction for each sample.

Definition at line 285 of file score.h.

Member Function Documentation

eval() [1/2]

double slope::MisClass::eval ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y, const std::unique_ptr< Loss > & loss ) const

overridevirtual

Evaluates the misclassification rate between predictions and true labels.

Parameters

eta	Matrix of model predictions/probabilities
y	Matrix of true labels (one-hot encoded)
loss	Loss function used to transform predictions if needed

Returns

Proportion of incorrect predictions (between 0 and 1)

Implements slope::Score.

eval() [2/2]

double slope::MisClass::eval ( const Eigen::MatrixXd & eta, const Eigen::MatrixXd & y, const std::unique_ptr< Loss > & loss, const Eigen::VectorXd & observation_weights ) const

overridevirtual

Evaluates the metric with observation weights. Empty weights select the unweighted metric. Weights must be finite and nonnegative, and their sum must be positive. Multiplying every weight by a positive constant leaves the score unchanged. Custom metrics can override this overload; the default implementation accepts uniform weights only.

Reimplemented from slope::Score.

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The documentation for this class was generated from the following file:

• include/slope/score.h