Model Evaluation
Mean Absolute Error
Average Miss Size
Regression evaluation often summarizes how far predictions are from actual values. Absolute errors ignore direction.
Program
Play the script to shift predictions and compare the resulting mean absolute error.
mean_absolute_error.R
actual <- c(10, 12, 13, 15)
adjustment <-
predicted <- c(9, 12, 14, 14) + adjustment
errors <- actual - predicted
mae <- mean(abs(errors))
label <- paste("mae", mae, sep = "=")
cat(label, "\n", sep = "")
actual <- c(10, 12, 13, 15)
adjustment <-
predicted <- c(9, 12, 14, 14) + adjustment
errors <- actual - predicted
mae <- mean(abs(errors))
label <- paste("mae", mae, sep = "=")
cat(label, "\n", sep = "")
actual <- c(10, 12, 13, 15)
adjustment <-
predicted <- c(9, 12, 14, 14) + adjustment
errors <- actual - predicted
mae <- mean(abs(errors))
label <- paste("mae", mae, sep = "=")
cat(label, "\n", sep = "")
residual
`actual - predicted` computes signed prediction errors.
absolute error
`abs(errors)` turns misses into non-negative distances.
MAE
`mean(abs(errors))` summarizes average miss size.