themis
1.0.3Extra Recipes Steps for Dealing with Unbalanced Data
Overview
A dataset with an uneven number of cases in each class is said to be unbalanced. Many models produce a subpar performance on unbalanced datasets. A dataset can be balanced by increasing the number of minority cases using SMOTE 2011 doi:10.48550/arXiv.1106.1813, BorderlineSMOTE 2005 doi:10.1007/11538059_91 and ADASYN 2008 https://ieeexplore.ieee.org/document/4633969. Or by decreasing the number of majority cases using NearMiss 2003 https://www.site.uottawa.ca/~nat/Workshop2003/jzhang.pdf or Tomek link removal 1976 https://ieeexplore.ieee.org/document/4309452.
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- Return-value docs
- 94%
- References docs
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12 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
- 1.0.3Latest
- RR 4.4.0 released · 2024-04-24
- 1.0.22023-08-14 · diff ↗
- RR 4.3.0 released · 2023-04-21
- 1.0.12023-04-15 · diff ↗
- 1.0.02022-07-02 · diff ↗
- 0.2.22022-05-12 · diff ↗
- RR 4.2.0 released · 2022-04-22
- 0.2.12022-04-13 · diff ↗
- 0.2.02022-03-30 · diff ↗
- 0.1.42021-06-12 · diff ↗
- RR 4.1.0 released · 2021-05-18
- 0.1.32020-11-12 · diff ↗
- 0.1.22020-08-14 · diff ↗
Show 4 earlier events
- 0.1.12020-05-17 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 0.1.02020-01-13
- RR 3.6.0 released · 2019-04-26
Package metadata
- First published
- 2020-01-13
- Total releases
- 12 / 6 yrs
- License
- MIT + file LICENSE OSI
- Minimum R
- ≥ 3.6
- Bundled data
- 5.3 KB / 1 file
- Download size
- 159 KB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet