{"id":6876,"date":"2025-09-16T07:03:40","date_gmt":"2025-09-16T07:03:40","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/09\/16\/a-visual-guide-to-tuning-gradient-boosted-trees\/"},"modified":"2025-09-16T07:03:40","modified_gmt":"2025-09-16T07:03:40","slug":"a-visual-guide-to-tuning-gradient-boosted-trees","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/09\/16\/a-visual-guide-to-tuning-gradient-boosted-trees\/","title":{"rendered":"A Visual Guide to Tuning Gradient Boosted Trees"},"content":{"rendered":"<p>    A Visual Guide to Tuning Gradient Boosted Trees<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>Introduction My previous posts looked at the bog-standard decision tree and the wonder of a random forest. Now, to complete the triplet, I\u2019ll visually explore gradient boosted trees! There are a bunch of gradient boosted tree libraries, including XGBoost, CatBoost, and LightGBM. However, for this I\u2019m going to use sklearn\u2019s one. Why? Simply because, compared [\u2026]<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/a-visual-guide-to-tuning-gradient-boosted-trees\/\">A Visual Guide to Tuning Gradient Boosted Trees<\/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    James Gibbins<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/towardsdatascience.com\/a-visual-guide-to-tuning-gradient-boosted-trees\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>A Visual Guide to Tuning Gradient Boosted Trees Introduction My previous posts looked at the bog-standard decision tree and the wonder of a random forest. Now, to complete the triplet, I\u2019ll visually explore gradient boosted trees! There are a bunch of gradient boosted tree libraries, including XGBoost, CatBoost, and LightGBM. However, for this I\u2019m going [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,83,82,67,1256,1240,70],"tags":[3793,379,1211],"class_list":["post-6876","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-data-science","category-data-visualization","category-deep-dives","category-gradient-boosting","category-hyperparameter-tuning","category-machine-learning","tag-boosted","tag-gradient","tag-trees"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6876"}],"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=6876"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/6876\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=6876"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=6876"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=6876"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}