smape: Symmetric Mean Absolute Percentage Error

In Metrics: Evaluation Metrics for Machine Learning

Description

smape computes the symmetric mean absolute percentage error between two numeric vectors.

Usage

smape(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

smape is defined as two times the average of abs(actual - predicted) / (abs(actual) + abs(predicted)). Therefore, at the elementwise level, it will provide NaN only if actual and predicted are both zero. It has an upper bound of 2, when either actual or predicted are zero or when actual and predicted are opposite signs.

smape is symmetric in the sense that smape(x, y) = smape(y, x).

See Also

mape mase

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)
smape(actual, predicted)

Example output

[1] 0.09333984

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