ZeroOneLoss {MLmetrics} | R Documentation |
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)
[Package MLmetrics version 1.1.3 Index]