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.


News

Version 0.1.1 (2012-06-19)

  • initial release
  • 16 evaluation metrics implemented with test cases

Reference manual

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install.packages("Metrics")

0.1.4 by Michael Frasco, 3 years ago


https://github.com/mfrasco/Metrics


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