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quant-ml-purged-cv-integration
將 Purged CV 整合到 ML 訓練流程的標準模式
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
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將 Purged CV 整合到 ML 訓練流程的標準模式
Codex 또는 Claude로 설치 이 Prompt를 복사해 Codex, Claude 또는 다른 어시스턴트에 붙여 넣으면 Skill 페이지를 검토하고 설치를 진행할 수 있습니다.
SOC 직업 분류 기준
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。
Creating algorithmic art using p5.js with seeded randomness and interactive parameter exploration. Use this when users request creating art using code, generative art, algorithmic art, flow fields, or particle systems. Create original algorithmic art rather than copying existing artists' work to avoid copyright violations.
Suite of tools for creating elaborate, multi-component claude.ai HTML artifacts using modern frontend web technologies (React, Tailwind CSS, shadcn/ui). Use for complex artifacts requiring state management, routing, or shadcn/ui components - not for simple single-file HTML/JSX artifacts.
You MUST use this before any creative work - creating features, building components, adding functionality, or modifying behavior. Explores user intent, requirements and design before implementation.
Applies Anthropic's official brand colors and typography to any sort of artifact that may benefit from having Anthropic's look-and-feel. Use it when brand colors or style guidelines, visual formatting, or company design standards apply.
Create beautiful visual art in .png and .pdf 文檔 using design philosophy. You should use this skill when the user asks to create a poster, piece of art, design, or other static piece. Create original visual designs, never copying existing artists' work to avoid copyright violations.
| 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