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: |
manymodelr |
| Reverse imports: |
audrex, ConsReg, dblr, deepregression, iml, immuneSIM, kssa, lilikoi, MetaIntegrator, populR, predtoolsTS, previsionio, PUPAIM, PUPAK, PUPMSI, RSCAT, sense, sjSDM, SPOTMisc, superml, VARMER, WaveletANN, workboots |
| Reverse suggests: |
featurefinder, luz, s2net, tfdatasets |
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