NMSE (Normalized Mean Squared Error)

`nmse()` computes the Normalized Mean Squared Error between the output of a regression model and the actual values of the target.

Usage

nmse(target, pred)

Arguments

target

Numeric vector containing the actual values.

pred

Numeric vector containing the predicted values. (The order should be the same than in the target)

Value

The normalized mean squared error (a single value).

Details

The Normalized Mean Squared error is defined as:

$$NMSE=MSE/((N-1)*var(target))$$

where MSE is the Mean Squared Error.

Examples

y <- 1:10
y_pred <- y+rnorm(10)
nmse(y,y_pred)
#> [1] 0.04578627