Metrics: Evaluation Metrics for Machine Learning

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 <mfrasco6 at>
License: BSD_3_clause + file LICENSE
NeedsCompilation: no
CRAN checks: Metrics results


Reference manual: Metrics.pdf


Package source: Metrics_0.1.4.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): Metrics_0.1.4.tgz, r-oldrel (arm64): Metrics_0.1.4.tgz, r-release (x86_64): Metrics_0.1.4.tgz, r-oldrel (x86_64): Metrics_0.1.4.tgz
Old sources: Metrics archive

Reverse dependencies:

Reverse depends: Greymodels, manymodelr, SAMprior
Reverse imports: ARGOS, audrex, ConsReg, coursekata, dblr, epicasting,, hybridts, ImFoR, iml, immuneSIM, janus, kssa, lilikoi, MetaIntegrator, mlr3shiny, OptiSembleForecasting, phytoclass, poolHelper, populR, predtoolsTS, previsionio, PUPAIM, PUPAK, PUPMSI, PWEV, RSCAT, RSP, sense, sjSDM, superml, WaveletANN, WaveletETS, WaveletGBM, WaveletKNN
Reverse suggests: cv, featurefinder, luz, s2net, tfdatasets


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