https://github.com/akbaritabar/gender-classification-precision-recall-f1-score

Calculating precision recall f1-score for gender classification methods
https://github.com/akbaritabar/gender-classification-precision-recall-f1-score

Last synced: 10 days ago
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Calculating precision recall f1-score for gender classification methods

• Host: GitHub
• URL: https://github.com/akbaritabar/gender-classification-precision-recall-f1-score
• Owner: akbaritabar
• License: gpl-3.0
• Created: 2024-12-07T11:09:57.000Z (17 days ago)
• Default Branch: main
• Last Pushed: 2024-12-07T11:43:17.000Z (17 days ago)
• Last Synced: 2024-12-07T12:19:01.929Z (17 days ago)
• Language: HTML
• Size: 443 KB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Gender classification and calculating precision, recall, and f1-score to compare two different classification methods

This is an illustrative example of how we can compare gender classification predicted by a machine, algorithm, or method with the reported gender.

We would need to calculate precision, recall, and f1-score to show the coverage, completeness, and relative comparison of these two measures, i.e., f1-score.

Here, I create a toy example with some fake data and show the calculation in Python by writing the formulas and the implementation from Scikit-Learn that does this conveniently with functions.

See the example Python script with in-line comments describing the steps in `2_illustrative_example_precision_recall.py`, a Jupyter notebook in `2_illustrative_example_precision_recall.ipynb`, its exported HTML in `2_illustrative_example_precision_recall.html` and PDF in `2_illustrative_example_precision_recall.pdf`.