{"id":5274,"date":"2025-07-14T07:02:22","date_gmt":"2025-07-14T07:02:22","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/07\/14\/how_do_you_efficiently_traverse_hundreds_of\/"},"modified":"2025-07-14T07:02:22","modified_gmt":"2025-07-14T07:02:22","slug":"how_do_you_efficiently_traverse_hundreds_of","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/07\/14\/how_do_you_efficiently_traverse_hundreds_of\/","title":{"rendered":"How do you efficiently traverse hundreds of features in the dataset?"},"content":{"rendered":"<p>    How do you efficiently traverse hundreds of features in the dataset?<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>Currently, working on a fintech classification algorithm, with close to a thousand features which is very tiresome. I&#8217;m not a domain expert, so creating sensible hypotesis is difficult. How do you tackle EDA and forming reasonable hypotesis in these cases? Even with proper documentation it&#8217;s not a trivial task to think of all interesting relationships that might be worth looking at. What I&#8217;ve been looking so far to make is:<\/p>\n<p>1) Baseline models and feature relevance assessment with in ensemble tree and via SHAP values<br \/> 2) Traversing features manually and check relationships that &#8220;make sense&#8221; for me<\/p>\n<\/p><\/div>\n<p><!-- SC_ON -->   submitted by   <a href=\"https:\/\/www.reddit.com\/user\/Grapphie\"> \/u\/Grapphie <\/a> <br \/> <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1ly06nw\/how_do_you_efficiently_traverse_hundreds_of\/\">[link]<\/a><\/span>   <span><a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1ly06nw\/how_do_you_efficiently_traverse_hundreds_of\/\">[comments]<\/a><\/span>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    \/u\/Grapphie<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/www.reddit.com\/r\/datascience\/comments\/1ly06nw\/how_do_you_efficiently_traverse_hundreds_of\/\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>How do you efficiently traverse hundreds of features in the dataset? Currently, working on a fintech classification algorithm, with close to a thousand features which is very tiresome. I&#8217;m not a domain expert, so creating sensible hypotesis is difficult. How do you tackle EDA and forming reasonable hypotesis in these cases? Even with proper documentation [&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":[385,117,7],"class_list":["post-5274","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-datascience","tag-do","tag-features","tag-how"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5274"}],"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=5274"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/5274\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=5274"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=5274"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=5274"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}