{"id":1428,"date":"2025-01-25T07:02:47","date_gmt":"2025-01-25T07:02:47","guid":{"rendered":"https:\/\/mailitics.com\/index.php\/2025\/01\/25\/avoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023\/"},"modified":"2025-01-25T07:02:47","modified_gmt":"2025-01-25T07:02:47","slug":"avoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023","status":"publish","type":"post","link":"https:\/\/mailitics.com\/index.php\/2025\/01\/25\/avoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023\/","title":{"rendered":"Avoid These Easily Missed Mistakes in Machine Learning Workflows\u200a\u2014\u200aPart 2"},"content":{"rendered":"<p>    Avoid These Easily Missed Mistakes in Machine Learning Workflows\u200a\u2014\u200aPart 2<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n    <!-- no image --><br \/>\n \t<BR><br \/>\n<BR><\/BR><\/p>\n<div>\n<div class=\"medium-feed-item\">\n<p class=\"medium-feed-image\"><a href=\"https:\/\/towardsdatascience.com\/avoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023\"><img data-recalc-dims=\"1\" decoding=\"async\" src=\"https:\/\/i0.wp.com\/cdn-images-1.medium.com\/max\/870\/1%2AchyYE1DmC2NuFw_Ua-ks7Q.png?w=870&#038;ssl=1\" ><\/a><\/p>\n<p class=\"medium-feed-snippet\">Using Unavailable Data at Prediction Time and Mixing Magic Numbers with Real Numbers<\/p>\n<p class=\"medium-feed-link\"><a href=\"https:\/\/towardsdatascience.com\/avoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023\">Continue reading on Towards Data Science \u00bb<\/a><\/p>\n<\/div>\n<\/div>\n<p> \t<BR><br \/>\n <BR><\/BR><br \/>\n    Thomas A Dorfer<br \/>\n \t<BR><br \/>\n<BR><\/BR><br \/>\n<a href=\"https:\/\/medium.com\/m\/global-identity-2?redirectUrl=https%3A%2F%2Ftowardsdatascience.com%2Favoid-these-easily-missed-mistakes-in-machine-learning-workflows-part-2-a1c6834d3023\">Go to original source<\/a><br \/>\n \t<BR><br \/>\n <BR><\/BR><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Avoid These Easily Missed Mistakes in Machine Learning Workflows\u200a\u2014\u200aPart 2 Using Unavailable Data at Prediction Time and Mixing Magic Numbers with Real Numbers Continue reading on Towards Data Science \u00bb Thomas A Dorfer Go to original source<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[62,69,83,70,520,1458],"tags":[1459,1460,564],"class_list":["post-1428","post","type-post","status-publish","format-standard","hentry","category-aimldsaimlds","category-artificial-intelligence","category-data-science","category-machine-learning","category-predictive-modeling","category-troubleshooting","tag-avoid","tag-easily","tag-these"],"_links":{"self":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1428"}],"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=1428"}],"version-history":[{"count":0,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/posts\/1428\/revisions"}],"wp:attachment":[{"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/media?parent=1428"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/categories?post=1428"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/mailitics.com\/index.php\/wp-json\/wp\/v2\/tags?post=1428"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}