scutr
0.2.0Balancing Multiclass Datasets for Classification Tasks
Overview
Imbalanced training datasets impede many popular classifiers. To balance training data, a combination of oversampling minority classes and undersampling majority classes is useful. This package implements the SCUT (SMOTE and Cluster-based Undersampling Technique) algorithm as described in Agrawal et. al. (2015) doi:10.5220/0005595502260234. Their paper uses model-based clustering and synthetic oversampling to balance multiclass training datasets, although other resampling methods are provided in this package.
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Health
- OK2026-06-0813 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- WARNING2026-06-0712 OK · 0 NOTE · 1 WARNING · 0 ERROR · 0 FAILURE
- OK2026-03-1014 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
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Code & Tests
- Cyclomatic complexity
- 3.0 median / 12 max
- Test cases
- 22 / 0.48 per code line
- Documented parameters
- 98%
Test coverage
Line coverage
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Tests / Examples
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Functions
11 10 exported
Complexity
3.5 avg / 12 max
Call network
11 nodes / 5 edges
Test coverage has not been measured for this package yet; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
People & History
2 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- 0.2.0Latest
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 0.1.22021-06-24
- RR 4.1.0 released · 2021-05-18
Package metadata
- First published
- 2021-06-24
- Total releases
- 2 / 5 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 174 KB / 3 files
- Download size
- 204 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet