模型调优与优化规则 (Model Optimization Guide)

What problem does it solve?

This guide helps you improve lottery prediction model performance by documenting repeatable hyperparameter tuning strategies and system-level fixes across multiple model families.

Core Features & Use Cases

• Hyperparameter optimization recipes: Provides concrete Random Search/Grid Search/Optuna parameter settings for multiple models (e.g., RF/XGBoost/LightGBM/CatBoost, LSTM, SM similarity, HMM, EVT, GA).
• Lottery-specific methodology: Covers双色球 SSQ-focused tuning for red/blue balls, including separate-pool handling and evaluation metrics like hit rate/top ranks and average rank.
• Production-oriented debugging: Records key integration fixes such as blue_config parameter injection and tie-breaker noise to prevent deterministic “dead-loop” outputs.

Quick Start

Ask the AI to summarize the best-practice tuning settings for SSQ red/blue balls from this guide and propose an updated Optuna search plan that matches your evaluation targets.