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將 Purged CV 整合到 ML 訓練流程的標準模式
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Based on SOC occupation classification
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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| 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