MLmetrics (version 1.1.0)

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

Description

Compute the normalized zero-one classification loss.

Usage

ZeroOneLoss(y_pred, y_true)

Arguments

y_pred

Predicted labels vector, as returned by a classifier

y_true

Ground truth (correct) 0-1 labels vector

Value

• Zero-One Loss

Examples

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