{"id":8987,"date":"2025-12-10T07:02:24","date_gmt":"2025-12-10T07:02:24","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/12\/10\/the-machine-learning-advent-calendar-day-9-lof-in-excel\/"},"modified":"2025-12-10T07:02:24","modified_gmt":"2025-12-10T07:02:24","slug":"the-machine-learning-advent-calendar-day-9-lof-in-excel","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/12\/10\/the-machine-learning-advent-calendar-day-9-lof-in-excel\/","title":{"rendered":"The Machine Learning \u201cAdvent Calendar\u201d Day 9: LOF in Excel"},"content":{"rendered":"<p>    The Machine Learning \u201cAdvent Calendar\u201d Day 9: LOF in Excel<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 this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of unsupervised learning: there is no single \u201ctrue\u201d outlier, only definitions. Understanding these definitions is the real skill.<\/p>\n<p>The post <a href=\"https:\/\/towardsdatascience.com\/the-machine-learning-advent-calendar-day-9-lof-in-excel\/\">The Machine Learning \u201cAdvent Calendar\u201d Day 9: LOF in Excel<\/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-9-lof-in-excel\/\">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 9: LOF in Excel In this article, we explore LOF through three simple steps: distances and neighbors, reachability distances, and the final LOF score. Using tiny datasets, we see how two anomalies can look obvious to us but completely different to different algorithms. This reveals the key idea of [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,83,70],"tags":[199,4401,341],"class_list":["post-8987","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-data-science","category-machine-learning","tag-learning","tag-lof","tag-machine"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8987"}],"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=8987"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/8987\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=8987"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=8987"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=8987"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}