The goal of this package is to provide weighted versions of metrics, scoring functions and performance measures for machine learning.


You can install the released version of MetricsWeighted from CRAN with:


To get the bleeding edge version, you can run



There are two ways to apply the package. We will go through them in the following examples. Please have a look at the vignette on CRAN for further information and examples.

Example 1: Directly apply the metrics


y <- 1:10
pred <- c(2:10, 14)

rmse(y, pred)
rmse(y, pred, w = 1:10)

r_squared(y, pred)
r_squared(y, pred, deviance_function = deviance_gamma)

Example 2: Call the metrics through a common function that can be used within a dplyr chain

dat <- data.frame(y = y, pred = pred)

performance(dat, actual = "y", predicted = "pred")
performance(dat, actual = "y", predicted = "pred", metrics = r_squared)
performance(dat, actual = "y", predicted = "pred", 
            metrics = list(rmse = rmse, `R-squared` = r_squared))
performance(dat, actual = "y", predicted = "pred",
            metrics = list(deviance = deviance_gamma, pseudo_r2 = r_squared_gamma))