Package: IRon 0.1.4

Nuno Moniz

IRon: Solving Imbalanced Regression Tasks

Imbalanced domain learning has almost exclusively focused on solving classification tasks, where the objective is to predict cases labelled with a rare class accurately. Such a well-defined approach for regression tasks lacked due to two main factors. First, standard regression tasks assume that each value is equally important to the user. Second, standard evaluation metrics focus on assessing the performance of the model on the most common cases. This package contains methods to tackle imbalanced domain learning problems in regression tasks, where the objective is to predict extreme (rare) values. The methods contained in this package are: 1) an automatic and non-parametric method to obtain such relevance functions; 2) visualisation tools; 3) suite of evaluation measures for optimisation/validation processes; 4) the squared-error relevance area measure, an evaluation metric tailored for imbalanced regression tasks. More information can be found in Ribeiro and Moniz (2020) <doi:10.1007/s10994-020-05900-9>.

Authors:Nuno Moniz [cre, aut], Rita P. Ribeiro [aut], Miguel Margarido [ctb]

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Peer review:

Bug tracker:https://github.com/nunompmoniz/iron/issues

On CRAN:

evaluation-metricsimbalance-dataimbalanced-learningmachine-learningregression

6 exports 18 stars 1.92 score 70 dependencies 38 scripts 266 downloads

Last updated 1 years agofrom:e7ad573698. Checks:OK: 9. Indexed: yes.

Exports:eval.statsphiphi.controlphiPlotsersera

Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecorrplotcowplotcpp11DEoptimRDerivdoBydplyrfansifarvergenericsggplot2ggpubrggrepelggsciggsignifgluegridExtragtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamodelrmunsellnlmenloptrnnetnumDerivpbkrtestpillarpkgconfigpolynompurrrquantregR6RColorBrewerRcppRcppEigenrlangrobustbaserstatixscalesscamSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr