Solving 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.
Install
Health
- OK13 OK2026-08-05
- NOTE12 OK, 1 NOTE2026-08-01
- OK13 OK2026-06-09
- ERROR12 OK, 1 ERROR2026-06-08
- OK14 OK2026-03-10
Documentation
- Help pages
- 16 (8 internal)
- Examples that run
- 100%8 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 6%
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Previously maintained by Nuno Moniz (0.1.0 to 0.1.4).
Pick two releases to compare their code metrics.
- 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 ↗
- unarchivedReturned to CRAN2022-06-14
- 0.1.22022-06-14 · diff ↗
- 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
- Minimum R
- ≥ 2.10
- Bundled data
- 544 KB / 2 files
- Download size
- 566 KB
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