IRon
0.1.5Solving Imbalanced Regression Tasks
Overview
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.
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- OK2026-06-0913 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-0812 OK · 0 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Checks run against github.com/nunompmoniz/iron on 2026-07-19.
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People & History
5 releases. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- 0.1.5Latest
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- 0.1.42023-01-20 · diff ↗
- 0.1.32022-10-29 · diff ↗
- 0.1.22022-06-14 · diff ↗
- unarchivedReturned to CRAN2022-06-14
- archivedRemoved from CRAN2022-06-12check issues were not corrected in time
- 0.1.02022-05-27
- RR 4.2.0 released · 2022-04-22
Package metadata
- First published
- 2022-05-27
- Total releases
- 5 / 4 yrs
- License
- CC0
- Download size
- 566 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet