MLmetrics (version 1.0.0)

ZeroOneLoss: Normalized Zero-One Loss (Classification Error Loss)

Description

Compute the zero-one classification loss.

Usage

ZeroOneLoss(y_true, y_pred)

Arguments

y_true

Ground truth (correct) labels vector

y_pred

Predicted labels vector, as returned by a classifier

Value

• Zero-One Loss

Examples

data(cars)
logreg <- glm(formula=vs~hp+wt, family=binomial(link = "logit"), mtcars)
pred <- ifelse(logreg$fitted.values<0.5, 0, 1)
ZeroOneLoss(y_true=mtcars$vs, y_pred=pred)