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

In MLmetrics: Machine Learning Evaluation Metrics

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)

Example output

Attaching package: 'MLmetrics'

The following object is masked from 'package:base':

    Recall

[1] 0.125

MLmetrics documentation built on May 2, 2019, 11:28 a.m.