ce: Classification Error

In Metrics: Evaluation Metrics for Machine Learning

Description

ce is defined as the proportion of elements in actual that are not equal to the corresponding element in predicted.

Usage

ce(actual, predicted)

Arguments

actual	The ground truth vector, where elements of the vector can be any variable type.
predicted	The predicted vector, where elements of the vector represent a prediction for the corresponding value in actual.

See Also

accuracy

Examples

actual <- c('a', 'a', 'c', 'b', 'c')
predicted <- c('a', 'b', 'c', 'b', 'a')
ce(actual, predicted)

Example output

[1] 0.4

Metrics documentation built on May 1, 2019, 10:11 p.m.