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    <title>ColumnTransformer on 每日拍拍</title>
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      <title>scikit-learn Pipeline 實戰：前處理、交叉驗證與避免資料洩漏</title>
      <link>https://dailypypy.org/learn/sklearn-pipeline-columntransformer/</link>
      <pubDate>Sun, 06 Sep 2026 11:08:00 +0800</pubDate>
      
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1440x768
prompt: masterpiece, best quality, highres, clean anime illustration, japanese anime style, soft shading, flat color design, 1girl, black hair, green eyes, white off-shoulder shirt, black short skirt, bust shot, front-facing eye-level view, arms crossed, confident gentle smile, looking at viewer, pale peach background, subtle floating connected blank geometric blocks and soft branching pathways without symbols or text, neat composition, detailed eyes, cute and smart vibe, minimal background, polished illustration, no text
negative prompt: worst quality, bad eye, bad hand, extra limbs, manga, multiple views, monochrome, text, signature
dedup note: The 155-post inventory contains no scikit-learn tutorial. The nearest item, ../python-uv/, only names scikit-learn in an installation example and does not discuss estimators, preprocessing, Pipeline, ColumnTransformer, cross-validation, or leakage. This article fills that missing end-to-end tabular ML workflow and does not repeat the NumPy, pandas reporting, PyTorch, or MLX articles.
source check: Reviewed against the current official scikit-learn documentation on 2026-09-06 for Pipeline, ColumnTransformer, cross-validation, common leakage pitfalls, nested parameter names, feature names, and model persistence.
---&gt;
&lt;p&gt;機器學習 Demo 最常見的樣子，是先把整份資料補值、標準化、轉成 one-hot，最後才切 train/test。&lt;/p&gt;</description>
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