<?xml version="1.0" encoding="UTF-8"?>
<rss version="2.0">
  <channel>
    <title>PyPI recent updates for automated-ml-pack</title>
    <link>https://pypi.org/project/automated-ml-pack/</link>
    <description>Recent updates to the Python Package Index for automated-ml-pack</description>
    <language>en</language>    <item>
      <title>1.0.7</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.7/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Thu, 18 Apr 2024 08:03:48 GMT</pubDate>
    </item>    <item>
      <title>1.0.6</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.6/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Tue, 26 Mar 2024 21:48:35 GMT</pubDate>
    </item>    <item>
      <title>1.0.5</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.5/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Fri, 22 Mar 2024 12:12:01 GMT</pubDate>
    </item>    <item>
      <title>1.0.4</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.4/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Wed, 20 Mar 2024 08:45:23 GMT</pubDate>
    </item>    <item>
      <title>1.0.3</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.3/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Tue, 19 Mar 2024 12:41:00 GMT</pubDate>
    </item>    <item>
      <title>1.0.2</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.2/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Sun, 17 Mar 2024 23:18:47 GMT</pubDate>
    </item>    <item>
      <title>1.0.1</title>
      <link>https://pypi.org/project/automated-ml-pack/1.0.1/</link>
      <description>This package is designed for swift and automated machine learning practice, catering to both classification and regression tasks. It facilitates model training, grid search application, and the preservation of the best model. Furthermore, it stores and visualizes the best scores attained by other models using commonly employed evaluation metrics.</description>
<author>cyrillemesue@gmail.com</author>      <pubDate>Sun, 17 Mar 2024 21:40:42 GMT</pubDate>
    </item>  </channel>
</rss>