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,UEI,valueprhr,WaveletANN,WaveletETS,WaveletGBM,WaveletKNN |
| Reverse suggests: | cv,httkexamples,luz,PatientLevelPrediction,s2net,tfdatasets |
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