npcs (version 0.1.1)

error_rate: Calculate the error rates for each class.

Description

Calculate the error rate for each class given the predicted labels and true labels.

Usage

error_rate(y.pred, y, class.names = NULL)

Value

A vector of the error rate for each class. The vector name is the same as class.names.

Arguments

y.pred

the predicted labels.

y

the true labels.

class.names

the names of classes. Should be a string vector. Default = NULL, which will set the name as 1, ..., K, where K is the number of classes.

References

Tian, Y., & Feng, Y. (2021). Neyman-Pearson Multi-class Classification via Cost-sensitive Learning. Submitted. Available soon on arXiv.

See Also

npcs, predict.npcs, generate_data, gamma_smote.

Examples

# data generation
set.seed(123, kind = "L'Ecuyer-CMRG")
train.set <- generate_data(n = 1000, model.no = 1)
x <- train.set$x
y <- train.set$y

test.set <- generate_data(n = 1000, model.no = 1)
x.test <- test.set$x
y.test <- test.set$y

library(nnet)
fit.vanilla <- multinom(y~., data = data.frame(x = x, y = factor(y)), trace = FALSE)
y.pred.vanilla <- predict(fit.vanilla, newdata = data.frame(x = x.test))
error_rate(y.pred.vanilla, y.test)