{"id":13320,"date":"2023-12-18T13:17:06","date_gmt":"2023-12-18T18:17:06","guid":{"rendered":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=13320"},"modified":"2023-12-18T13:17:06","modified_gmt":"2023-12-18T18:17:06","slug":"fashion-repeats-itself-generating-tabular-data-via-diffusion-and-xgboost","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/fashion-repeats-itself-generating-tabular-data-via-diffusion-and-xgboost\/13320\/","title":{"rendered":"Fashion Repeats Itself: Generating Tabular Data Via Diffusion and XGBoost"},"content":{"rendered":"Originally published by Alexia Jolicoeur-Martineau, Sept 19, 2023. Since\u00a0AlexNet\u00a0showed the world the power of deep learning, the field of AI has rapidly switched to almost exclusively focus on deep learning. Some of the main justifications are that 1) neural networks are Universal Function Approximation (UFA, not UFO\u00a0\ud83d\udef8), 2) deep learning generally works the best, and 3) it is highly scalable through\u00a0SGD\u00a0and GPUs. However, when you look a bit further down from the surface, you see that 1)\u00a0simple methods such as Decision Trees are also UFAs, 2) fancy tree-based methods such as Gradient-Boosted Trees (GBTs) actually work better than <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/fashion-repeats-itself-generating-tabular-data-via-diffusion-and-xgboost\/13320\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Originally published by Alexia Jolicoeur-Martineau, Sept 19, 2023. Since\u00a0AlexNet\u00a0showed the world the power of deep learning, the field of AI has rapidly switched to almost exclusively focus on deep learning. Some of the main justifications are that 1) neural networks are Universal Function Approximation (UFA, not UFO\u00a0\ud83d\udef8), 2) deep learning generally works the best, and [&hellip;]<\/p>\n","protected":false},"author":72,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[879,1338,368,791,243,1337],"class_list":["post-13320","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand","tag-ai","tag-ai-data","tag-artificial-intelligence","tag-deep-learning","tag-machine-learning","tag-tabular-data"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/13320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/users\/72"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=13320"}],"version-history":[{"count":1,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/13320\/revisions"}],"predecessor-version":[{"id":13321,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/13320\/revisions\/13321"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=13320"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=13320"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=13320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}