Quality metrics

Quality metrics (score/loss functions) are used to quantify the performance of a given model. Score functions are typically maximized (e.g. accuracy, concordance, ...) whereas loss functions should be minimized (e.g. mean squared error, error rate, ...). Optunity provides common score/loss functions for your convenience.

We use the following calling convention:

• y (iterable): the true labels/function values
• yhat (iterable): the predicted labels/function values
• we assume y and yhat are of the same length (though we do not assert this).
• potential parameters of the score function must be added by keyword

All functions listed here are available in optunity.metrics.

Score functions

Score functions are typically maximized (e.g. optunity.maximize()).

Classification

Score	Associated Optunity function
accuracy	accuracy()
area under ROC curve	roc_auc()
area under PR curve	pr_auc()
\(F_\beta\)	fbeta()
precision/PPV	precision()
recall/sensitivity	recall()
specificity/NPV	npv()
PU score	pu_score()

Regression

Score	Associated Optunity function
R squared	r_squared()

Loss functions

Loss functions are typically minimized (e.g. optunity.minimize()).

Classification

Score	Associated Optunity function
Brier score	brier()
error rate	error_rate()
log loss	logloss()

Regression

Score	Associated Optunity function
mean squared error	mse()
absolute error	absolute_error()