6fe2158511
- Introduced a new plan to modify the Optuna objective function to prioritize precision under a recall constraint of 0.35, improving model performance in scenarios where false positives are costly. - Updated training scripts to implement precision-based metrics and adjusted the walk-forward cross-validation process to incorporate precision and recall calculations. - Enhanced the active LGBM parameters and training log to reflect the new metrics and model configurations. - Added a new design document outlining the implementation steps for the precision-focused optimization. This update aims to refine the model's decision-making process by emphasizing precision, thereby reducing potential losses from false positives.
1550 lines
32 KiB
JSON
1550 lines
32 KiB
JSON
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