rae: Relative Absolute Error

In Metrics: Evaluation Metrics for Machine Learning

Description

rae computes the relative absolute error between two numeric vectors.

Usage

rae(actual, predicted)

Arguments

actual	The ground truth numeric vector.
predicted	The predicted numeric vector, where each element in the vector is a prediction for the corresponding element in actual.

Details

rae divides sum(ae(actual, predicted)) by sum(ae(actual, mean(actual))), meaning that it provides the absolute error of the predictions relative to a naive model that predicted the mean for every data point.

See Also

rse rrse

Examples

actual <- c(1.1, 1.9, 3.0, 4.4, 5.0, 5.6)
predicted <- c(0.9, 1.8, 2.5, 4.5, 5.0, 6.2)
rrse(actual, predicted)

Example output

[1] 0.2043785

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