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 0.1.1 (2012-06-19)

  • initial release
  • 16 evaluation metrics implemented with test cases

Reference manual

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0.1.4 by Michael Frasco, 3 years ago


Report a bug at https://github.com/mfrasco/Metrics/issues

Browse source code at https://github.com/cran/Metrics

Authors: Ben Hamner [aut, cph] , Michael Frasco [aut, cre] , Erin LeDell [ctb]

Documentation:   PDF Manual  

BSD_3_clause + file LICENSE license

Suggests testthat

Imported by ConsReg, MetaIntegrator, RSCAT, VARMER, dblr, deepregression, iml, immuneSIM, lilikoi, predtoolsTS, previsionio, sense, specmine, superml.

Depended on by PUPAIM, manymodelr.

Suggested by featurefinder, luz, s2net, tfdatasets.

See at CRAN