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    <title>PyPI recent updates for cv-score-predict</title>
    <link>https://pypi.org/project/cv-score-predict/</link>
    <description>Recent updates to the Python Package Index for cv-score-predict</description>
    <language>en</language>    <item>
      <title>0.2.8</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.8/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Tue, 30 Jun 2026 15:14:21 GMT</pubDate>
    </item>    <item>
      <title>0.2.6</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.6/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Thu, 19 Mar 2026 08:43:52 GMT</pubDate>
    </item>    <item>
      <title>0.2.5</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.5/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Mon, 16 Mar 2026 10:18:38 GMT</pubDate>
    </item>    <item>
      <title>0.2.3</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.3/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Mon, 16 Mar 2026 08:51:10 GMT</pubDate>
    </item>    <item>
      <title>0.2.2</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.2/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Wed, 04 Mar 2026 11:21:04 GMT</pubDate>
    </item>    <item>
      <title>0.2.1</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.1/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Wed, 25 Feb 2026 09:46:07 GMT</pubDate>
    </item>    <item>
      <title>0.2.0</title>
      <link>https://pypi.org/project/cv-score-predict/0.2.0/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Tue, 10 Feb 2026 16:54:13 GMT</pubDate>
    </item>    <item>
      <title>0.1.9</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.9/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Tue, 27 Jan 2026 12:19:20 GMT</pubDate>
    </item>    <item>
      <title>0.1.8</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.8/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Tue, 27 Jan 2026 10:24:45 GMT</pubDate>
    </item>    <item>
      <title>0.1.7</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.7/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Mon, 12 Jan 2026 15:50:02 GMT</pubDate>
    </item>    <item>
      <title>0.1.6</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.6/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Sun, 11 Jan 2026 22:50:00 GMT</pubDate>
    </item>    <item>
      <title>0.1.5</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.5/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Sun, 11 Jan 2026 17:52:00 GMT</pubDate>
    </item>    <item>
      <title>0.1.4</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.4/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Fri, 09 Jan 2026 14:42:16 GMT</pubDate>
    </item>    <item>
      <title>0.1.3</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.3/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Fri, 09 Jan 2026 12:46:53 GMT</pubDate>
    </item>    <item>
      <title>0.1.2</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.2/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Fri, 09 Jan 2026 11:57:26 GMT</pubDate>
    </item>    <item>
      <title>0.1.1</title>
      <link>https://pypi.org/project/cv-score-predict/0.1.1/</link>
      <description>Cross-validated ensemble prediction with LGBM, XGBoost, and CatBoost — with safe categorical handling, multi-seed averaging, and artifact return.</description>
<author>danu@andries.lu</author>      <pubDate>Fri, 09 Jan 2026 10:54:19 GMT</pubDate>
    </item>  </channel>
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