Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

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: about 2 months ago
JSON representation

R package for weighted model metrics

Awesome Lists containing this project

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?branch=main)
[![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.