Add improvement notes and helper examples for user-provided optimized script

[kokibs]

Motivation

• The user-provided “optimized” training script had valid ideas but left risks around leakage, feature redundancy, and compute cost that reduce reproducibility and generalization.
• Provide a concise, actionable checklist and code snippets so readers can safely apply lag/rolling/EMA features, stabilize CV, and prioritize LightGBM tuning.

Description

• Appended a new section "7. 参考: ユーザー提示の最適化スクリプトに対する改善ポイント" to docs/brain_lever_competition_improvements.md that summarizes risks and recommendations across CV design, feature pruning, time-series handling, model selection, and numerical stability.
• Included concrete suggestions and best practices for CV (prefer GroupKFold(sample_id) with optional time-holdout), feature reduction (importance-based pruning), and safer time-series features (lag, diff, EMA, limited rolling windows).
• Added example helper functions and code snippets (add_rolling_features, add_ema_features, prepare_features, cv_score, train_and_predict) illustrating how to implement the recommended feature engineering and an LGBM-centered workflow with early stopping and regularization.

Testing

• No automated tests were run because this is a documentation-only change.
• There were no CI/unit test executions as part of this update.
• The documentation content was added and formatted inline with the existing file; no runtime code changes were introduced to production modules.

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Codex Task