feat: enhance precision optimization in model training

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- 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.

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21in7

2026-03-03 00:57:19 +09:00

parent 3613e3bf18

commit 6fe2158511

6 changed files with 1590 additions and 627 deletions

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models/active_lgbm_params.json

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