https://github.com/mayer79/metricsweighted

R package for weighted model metrics
https://github.com/mayer79/metricsweighted

machine-learning metrics performance r r-package rstats statistics

Last synced: 16 days ago
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R package for weighted model metrics

• Host: GitHub
• URL: https://github.com/mayer79/metricsweighted
• Owner: mayer79
• License: gpl-2.0
• Created: 2019-07-20T18:48:17.000Z (almost 6 years ago)
• Default Branch: main
• Last Pushed: 2025-04-12T18:26:33.000Z (3 months ago)
• Last Synced: 2025-06-29T17:03:47.089Z (16 days ago)
• Topics: machine-learning, metrics, performance, r, r-package, rstats, statistics
• Language: R
• Homepage: https://mayer79.github.io/MetricsWeighted/
• Size: 2.29 MB
• Stars: 11
• Watchers: 3
• Forks: 4
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • Changelog: NEWS.md
  • License: LICENSE.md

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README

        
# {MetricsWeighted} 

[![R-CMD-check](https://github.com/mayer79/MetricsWeighted/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/mayer79/MetricsWeighted/actions/workflows/R-CMD-check.yaml)

[![Codecov test coverage](https://codecov.io/gh/mayer79/MetricsWeighted/graph/badge.svg)](https://app.codecov.io/gh/mayer79/MetricsWeighted)

[![CRAN_Status_Badge](https://www.r-pkg.org/badges/version/MetricsWeighted)](https://cran.r-project.org/package=MetricsWeighted)

[![](https://cranlogs.r-pkg.org/badges/MetricsWeighted)](https://cran.r-project.org/package=MetricsWeighted) 

[![](https://cranlogs.r-pkg.org/badges/grand-total/MetricsWeighted?color=orange)](https://cran.r-project.org/package=MetricsWeighted)

## Overview

{MetricsWeighted} provides weighted and unweighted versions of metrics and performance measures for machine learning.

## Installation

```r

# From CRAN

install.packages("MetricsWeighted")

# Development version

devtools::install_github("mayer79/MetricsWeighted")

```

## Usage

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

``` r

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

Useful, e.g., in a {dplyr} chain.

``` r

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

```

Check out the vignette for more applications.