القائمة
將 Purged CV 整合到 ML 訓練流程的標準模式
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
| name | quant-ml-purged-cv-integration |
| description | 將 Purged CV 整合到 ML 訓練流程的標準模式 |
當在金融時間序列上訓練 ML 模型時，必須使用 Purged CV 而非標準 K-Fold，以防止數據洩露。
⚠️ 禁止 在金融序列上使用
sklearn.model_selection.KFold。 標準 K-Fold 會導致訓練集和測試集在時間上重疊，造成過擬合幻覺。
[!TIP] 已提取代碼至：quant-ml-purged-cv-integration_examples_1.py
| 參數 | 說明 | 建議值 |
|---|---|---|
purge_window | 測試集之前剔除的樣本數 | 標籤前視長度 (如 5 天) |
embargo_window | 測試集之後剔除的樣本數 | 序列相關性半衰期 |
n_test_splits | 測試集組數 | 1-2 (增加路徑多樣性) |
CombinatorialPurgedKFold 並整合至 MLP生成適用於金融機器學習的高質量特徵。包含分數階差分 (Fractional Differentiation) 與無前視偏差的滾動標準化。
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