{"id":8908,"date":"2025-12-07T07:02:26","date_gmt":"2025-12-07T07:02:26","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/07\/the-machine-learning-advent-calendar-day-6-decision-tree-regressor\/"},"modified":"2025-12-07T07:02:26","modified_gmt":"2025-12-07T07:02:26","slug":"the-machine-learning-advent-calendar-day-6-decision-tree-regressor","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/07\/the-machine-learning-advent-calendar-day-6-decision-tree-regressor\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Day 6: Decision Tree Regressor"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Day 6: Decision Tree Regressor<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<p>During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees.<br \/>\nWith a simple one-feature dataset, we can see how a tree chooses its first split. The idea is always the same: if humans can guess the split visually, then we can rebuild the logic step by step in Excel.<br \/>\nBy listing all possible split values and computing the MSE for each one, we identify the split that reduces the error the most. This gives us a clear intuition of how a Decision Tree grows, how it makes predictions, and why the first split is such a crucial step.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-day-6-decision-tree-regressor\/\">The Machine Learning \u201cAdvent Calendar\u201d Day 6: Decision Tree Regressor<\/a> appeared first on <a href=\"https:\/\/towardsdatascience.com\/\">Towards Data Science<\/a>.<\/p>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    angela shi<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-day-6-decision-tree-regressor\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>The Machine Learning \u201cAdvent Calendar\u201d Day 6: Decision Tree Regressor During the first days of this Machine Learning Advent Calendar, we explored models based on distances. Today, we switch to a completely different way of learning: Decision Trees. With a simple one-feature dataset, we can see how a tree chooses its first split. The idea [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,2076,69,83,1058,70],"tags":[480,199,1696],"class_list":["post-8908","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-algorithms","category-artificial-intelligence","category-data-science","category-decision-tree","category-machine-learning","tag-decision","tag-learning","tag-tree"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8908"}],"collection":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/comments?post=8908"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8908\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=8908"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=8908"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=8908"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}