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

In LqNoob/Machine-Learning-Evaluation-Metrics: 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)

LqNoob/Machine-Learning-Evaluation-Metrics documentation built on May 20, 2019, 3:33 p.m.