
  <rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
    <channel>
      <title>Data Awesome</title>
      <link>https://data-awesome.com/blog</link>
      <description>Writing on data analytics, Alteryx, AI and the career around them, from ten years leading analytics in airline operations.</description>
      <language>en-us</language>
      <managingEditor>racine.isacar@gmail.com (Isacar Racine)</managingEditor>
      <webMaster>racine.isacar@gmail.com (Isacar Racine)</webMaster>
      <lastBuildDate>Sat, 01 Aug 2026 00:00:00 GMT</lastBuildDate>
      <atom:link href="https://data-awesome.com/tags/machine-learning/feed.xml" rel="self" type="application/rss+xml"/>
      
  <item>
    <guid>https://data-awesome.com/blog/division-prueba-unica-comparar-modelos</guid>
    <title>Una sola división de prueba no basta para comparar modelos</title>
    <link>https://data-awesome.com/blog/division-prueba-unica-comparar-modelos</link>
    <description>Una sola división train/test puede hacer que un modelo se vea mejor de lo que es y otro peor, en el mismo experimento. Así se ve eso con números reales.</description>
    <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>model-diagnostics</category><category>statistics</category><category>machine-learning</category>
  </item>

  <item>
    <guid>https://data-awesome.com/blog/single-test-split-model-comparison</guid>
    <title>A Single Test Split Isn&#39;t Enough to Compare Models</title>
    <link>https://data-awesome.com/blog/single-test-split-model-comparison</link>
    <description>One train/test split can make one model look better than it is and another look worse, in the same experiment. Here&#39;s what that looks like with real numbers.</description>
    <pubDate>Sat, 01 Aug 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>model-diagnostics</category><category>statistics</category><category>machine-learning</category>
  </item>

  <item>
    <guid>https://data-awesome.com/blog/variable-selection-regularization</guid>
    <title>Variable Selection, Ridge, Lasso, Elastic Net</title>
    <link>https://data-awesome.com/blog/variable-selection-regularization</link>
    <description>Ridge, lasso and elastic net returned identical test error, to the last digit. The reason is a mistake hiding in a workflow you have copied.</description>
    <pubDate>Mon, 06 Apr 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>regression</category><category>machine-learning</category><category>statistics</category><category>r</category>
  </item>

  <item>
    <guid>https://data-awesome.com/blog/ai-analytics-guide</guid>
    <title>AI in Analytics: How to Leverage Machine Learning for Data-Driven Decisions</title>
    <link>https://data-awesome.com/blog/ai-analytics-guide</link>
    <description>Discover how AI and machine learning transform analytics. Learn practical applications, tools, and strategies to implement AI in your analytics workflow and make smarter business decisions.</description>
    <pubDate>Sat, 28 Feb 2026 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>ai</category><category>machine-learning</category><category>analytics</category>
  </item>

  <item>
    <guid>https://data-awesome.com/blog/ai-analytics-guia</guid>
    <title>IA en Análisis: Cómo Usar Machine Learning para Decisiones Basadas en Datos</title>
    <link>https://data-awesome.com/blog/ai-analytics-guia</link>
    <description>Descubre cómo IA y machine learning transforman el análisis. Aprende aplicaciones prácticas, herramientas y estrategias para implementar AI en tu flujo de análisis y tomar decisiones de negocio más inteligentes.</description>
    <pubDate>Mon, 20 Jan 2025 00:00:00 GMT</pubDate>
    <author>racine.isacar@gmail.com (Isacar Racine)</author>
    <category>ai</category><category>machine-learning</category><category>analytics</category>
  </item>

    </channel>
  </rss>
