Because I believe there’s still a niche for an R package that have all the following traits in one place:

• Simple

• Consistent interface

• Well-documented

• Well-tested

• Accurate and fast

Why do I think so? While doing my evaluation work on a machine learning project I found that there’s no single R package is on a par with scikit-learn’s metrics module in term of coverage, ease of use, throughout testing and documentaion richness. I’m not saying that these packages are terrible, they’re designed for a very specific use case(s)/problem(s) with varying quality.

• The two major framework for doing machine learning in R are caret and mlr(3). The next generation of caret is tinymodels of which parnship is the main package for performance metrics.

• pROC, precrec

• InformationValue

• Metrics, ModelMetrics

Install the stable version of metrics from CRAN:

install.packages("metrics")

Or install the development version from Github with:

devtools::install_github("chuvanan/metrics")

All metrics functions share the same interface: mtr_fun(actual, predicted) which is applicable to both classification and regression settings.

• mtr_ is the short form of metrics. As in stringr package, metrics uses the prefix to provide consistent naming that makes it easy to type with autocompletion in RStuido or Emacs’s ESS.

• _fun is the name of performance metrics. The package declares verbosely which measure is going to be used. For a full list of evaluation metrics, please see TODO.

• metrics package prefers convention over configuration. Argument actual, in context of classification tasks, stricly accepts binary values 0 and 1 where the former is negative class and the later is positive one.

Here’s a quick example:

library(metrics)

## simulate sample data set
set.seed(123)
preds <- runif(1000)
truth <- round(preds)
preds[sample(1000, 300)] <- runif(300) # noise

## overall accuracy
mtr_accuracy(truth, preds)              # default threshold is 0.5

## [1] 0.838

## precision
mtr_precision(truth, preds)

## [1] 0.82643

## recall
mtr_recall(truth, preds)

## [1] 0.8498986

## AUROC
mtr_auc_roc(truth, preds)

## [1] 0.8260939