hydropython/Interprtable-machine-learning-with-hyper-paramter-tuning
View on GitHub ↗Related repositories →applicability of regressors Random Forest (RF), Gradient Booting (GB), and Decision Tree (DT), K Nearest Neighbor (kNN), and XGBoost architectures to predict daily lake evaporation of five reservoirs in the Awash River basin, Ethiopia.
active 2024-06-21 → 2024-06-21 (UTC)
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Line chart, 1 days from 2024-06-21 to 2024-06-21. Pushes: 4 total, peak 4 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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Pushes, PRs, issues, reviews and comments — stars and forks excluded, so this is contribution rather than popularity
| Contributor | Contributions | Pushes | PRs | Comments |
|---|---|---|---|---|
| hydropython | 4 | 4 | 0 | 0 |
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