Metrics: Evaluation Metrics for Machine Learning (original) (raw)
An implementation of evaluation metrics in R that are commonly used in supervised machine learning. It implements metrics for regression, time series, binary classification, classification, and information retrieval problems. It has zero dependencies and a consistent, simple interface for all functions.
| Version: | 0.1.4 |
|---|---|
| Suggests: | testthat |
| Published: | 2018-07-09 |
| DOI: | 10.32614/CRAN.package.Metrics |
| Author: | Ben Hamner [aut, cph], Michael Frasco [aut, cre], Erin LeDell [ctb] |
| Maintainer: | Michael Frasco |
| BugReports: | https://github.com/mfrasco/Metrics/issues |
| License: | BSD_3_clause + file |
| URL: | https://github.com/mfrasco/Metrics |
| NeedsCompilation: | no |
| CRAN checks: | Metrics results |
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: | Greymodels, manymodelr, SAMprior |
|---|---|
| Reverse imports: | ai, ARGOS, audrex, ConsReg, coursekata, dblr, epicasting, gbm.auto, hybridts, ImFoR, iml, immuneSIM, janus, kssa, lilikoi, mlr3shiny, phytoclass, poolHelper, populR, predtoolsTS, previsionio, PUPAK, PUPMSI, PWEV, RSCAT, RSP, scoringutils, sense, sjSDM, superml, UEI, WaveletANN, WaveletETS, WaveletGBM, WaveletKNN |
| Reverse suggests: | cv, featurefinder, luz, s2net, tfdatasets |
Linking:
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