{"id":8743,"date":"2025-12-01T07:02:45","date_gmt":"2025-12-01T07:02:45","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/01\/shap_or_lgbm_gain_for_feature_selection\/"},"modified":"2025-12-01T07:02:45","modified_gmt":"2025-12-01T07:02:45","slug":"shap_or_lgbm_gain_for_feature_selection","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/01\/shap_or_lgbm_gain_for_feature_selection\/","title":{"rendered":"Shap or LGBM gain for feature selection?"},"content":{"rendered":"<p>    Shap or LGBM gain for feature selection?<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<!-- SC_OFF --><\/p>\n<div class=\"md\">\n<p>Which one do you use during recursive feature elimination or forward\/backward selection? I&#8217;ve always used gain and only used shap for analytics on model predictions, but came across some shap values recommendations.<\/p>\n<p>Bonus question: have you used &#8220;null importance&#8221; \/ permutation method? Fitting models with shuffled targets to remove features that look predictive by chance<\/p>\n<\/p><\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Nanirith\"> \/u\/Nanirith <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1p9rwhf\/shap_or_lgbm_gain_for_feature_selection\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1p9rwhf\/shap_or_lgbm_gain_for_feature_selection\/\">[comments]<\/a><\/span>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    \/u\/Nanirith<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1p9rwhf\/shap_or_lgbm_gain_for_feature_selection\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Shap or LGBM gain for feature selection? Which one do you use during recursive feature elimination or forward\/backward selection? I&#8217;ve always used gain and only used shap for analytics on model predictions, but came across some shap values recommendations. Bonus question: have you used &#8220;null importance&#8221; \/ permutation method? Fitting models with shuffled targets to [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,99],"tags":[321,3272,4325],"class_list":["post-8743","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-datascience","tag-feature","tag-gain","tag-shap"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8743"}],"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=8743"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8743\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=8743"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=8743"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=8743"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}