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.
Documentation:
Downloads:
Reverse dependencies:
| Reverse depends: |
Greymodels, NumericEnsembles |
| Reverse imports: |
adsoRptionCMF, ai, ARGOS, audrex, ConsReg, coursekata, dblr, epicasting, gbm.auto, hybridts, ImFoR, iml, janus, kssa, lilikoi, manymodelr, mlr3shiny, nda, phytoclass, poolHelper, populR, predtoolsTS, previsionio, PUPAK, PUPMSI, PWEV, RSCAT, SAMprior, scoringutils, sense, sjSDM, superml, UEI, WaveletANN, WaveletETS, WaveletGBM, WaveletKNN |
| Reverse suggests: |
cv, luz, PatientLevelPrediction, s2net, tfdatasets |
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