{"id":8921,"date":"2025-12-08T07:02:42","date_gmt":"2025-12-08T07:02:42","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/08\/the-machine-learning-advent-calendar-day-7-decision-tree-classifier\/"},"modified":"2025-12-08T07:02:42","modified_gmt":"2025-12-08T07:02:42","slug":"the-machine-learning-advent-calendar-day-7-decision-tree-classifier","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/08\/the-machine-learning-advent-calendar-day-7-decision-tree-classifier\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Day 7: Decision Tree Classifier"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Day 7: Decision Tree Classifier<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>In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error.<br \/>\nToday, for Day 7 of the Machine Learning &#8220;Advent Calendar&#8221;, we switch to classification. With just one numerical feature and two classes, we explore how a Decision Tree Classifier decides where to cut the data, using impurity measures like Gini and Entropy.<br \/>\nEven without doing the math, we can visually guess possible split points. But which one is best? And do impurity measures really make a difference? Let us build the first split step by step in Excel and see what happens.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-day-7-decision-tree-classifier\/\">The Machine Learning \u201cAdvent Calendar\u201d Day 7: Decision Tree Classifier<\/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-7-decision-tree-classifier\/\">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 7: Decision Tree Classifier In Day 6, we saw how a Decision Tree Regressor finds its optimal split by minimizing the Mean Squared Error. Today, for Day 7 of the Machine Learning &#8220;Advent Calendar&#8221;, we switch to classification. With just one numerical feature and two classes, we explore how [&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":[709,480,1696],"class_list":["post-8921","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-day","tag-decision","tag-tree"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8921"}],"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=8921"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8921\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=8921"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=8921"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=8921"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}