对 classification-model-training 的 baseline run 做超参调优（run_tuning.py：规则诊断 / 可选 Optuna 搜索）或特征筛选（select_features.py：按 PSI/IV/缺失率剔除特征），产 -tuned / -feat 新 run，按 baseline.algo 直通 xgb/dnn/lr。当用户说"调优""调参""优化模型""超参搜索""欠拟合""过拟合""不收敛""特征筛选""剔除高 PSI 特征""剔除低 IV 特征""剔除高缺失特征""缩小特征集"时使用。
How to install classification-model-tuning?
qfin-tech/model-evo/classification-model-tuning
$npx -y skills add qfin-tech/model-evo --skill classification-model-tuningInstalls into the current project.
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Run `npx skills use "https://github.com/qfin-tech/model-evo" --skill "qfin-tech/model-evo/classification-model-tuning"` and follow the generated skill instructions now. Read its complete output, redirecting it to a temporary file first if necessary. Resolve relative paths from the supporting-files directory it provides.
Use the skills in "https://github.com/qfin-tech/model-evo" that are relevant to the current task. Run `npx skills add "https://github.com/qfin-tech/model-evo"` and select the relevant skills, then follow their instructions.
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