A general outline for fraud detection model building is proposed, encompassing data preprocessing, feature engineering, data splitting, model selection, model training, model evaluation, imbalanced data handling, ensemble methods, threshold optimization, monitoring and updating, explainability and interpretability, and compliance and security.
active 2024-01-17 → 2024-01-17 (UTC)
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Line chart, 1 days from 2024-01-17 to 2024-01-17. Pushes: 1 total, peak 1 in a day. Pull requests: 0 total, peak 0 in a day. Issues: 0 total, peak 0 in a day. Comments: 0 total, peak 0 in a day. Stars: 0 total, peak 0 in a day.
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Top contributors
Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| havoc7 | 1 | 1 | 0 | 0 |
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Totals cover only the window loaded into ClickHouse and count events, not GitHub's lifetime totals — 0 stars here means stars gained during the window, not the repo's star count.