MetricsWeighted

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

Installation

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

install.packages("MetricsWeighted")

To get the bleeding edge version, you can run

library(devtools)
install_github("mayer79/MetricsWeighted")

Application

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: Standard interface

library(MetricsWeighted)

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

rmse(y, pred)            # 1.58
rmse(y, pred, w = 1:10)  # 1.93

r_squared(y, pred)       # 0.70
r_squared(y, pred, deviance_function = deviance_gamma)  # 0.78

Example 2: data.frame interface

Can e.g. be used in a dplyr chain.

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

performance(dat, actual = "y", predicted = "pred")

> metric    value
>   rmse 1.581139


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

>    metric     value
>      rmse 1.5811388
> R-squared 0.6969697