percent_bias: Percent Bias

In Metrics: Evaluation Metrics for Machine Learning

Description

percent_bias computes the average amount that actual is greater than predicted as a percentage of the absolute value of actual.

Usage

percent_bias(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

If a model is unbiased percent_bias(actual, predicted) should be close to zero. Percent Bias is calculated by taking the average of (actual - predicted) / abs(actual) across all observations.

percent_bias will give -Inf, Inf, or NaN, if any elements of actual are 0.

See Also

bias

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)
percent_bias(actual, predicted)

Example output

[1] 0.04520772

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