{"id":4241,"date":"2014-11-03T20:18:02","date_gmt":"2014-11-03T20:18:02","guid":{"rendered":"http:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/?p=4241"},"modified":"2014-11-03T20:18:34","modified_gmt":"2014-11-03T20:18:34","slug":"keys-avoiding-pitfalls-analytical-models-testing-relevancy","status":"publish","type":"post","link":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/keys-avoiding-pitfalls-analytical-models-testing-relevancy\/4241\/","title":{"rendered":"Keys to avoiding pitfalls on analytical models: testing, relevancy"},"content":{"rendered":"Predictive modeling can lead to some pretty bad insights when done poorly, but overcoming some common issues can help users sidestep problems on predictive analytics projects. Predictive modeling can be a powerful tool to help businesses see problems and opportunities that are coming their way, but when done poorly, it can lead them down a path of error and uncertainty. Understanding where the pitfalls lie is a must for getting the most out of your analytical models. For example, speaking at the 2014 Predictive Analytics World conference in Boston, John Elder, president of consulting firm Elder Research Inc., <a href=\"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/keys-avoiding-pitfalls-analytical-models-testing-relevancy\/4241\/\" class=\"more-link\">(more&hellip;)<\/a>","protected":false},"excerpt":{"rendered":"<p>Predictive modeling can lead to some pretty bad insights when done poorly, but overcoming some common issues can help users sidestep problems on predictive analytics projects. Predictive modeling can be a powerful tool to help businesses see problems and opportunities that are coming their way, but when done poorly, it can lead them down a [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":"","_links_to":"","_links_to_target":""},"categories":[11,48],"tags":[],"class_list":["post-4241","post","type-post","status-publish","format-standard","hentry","category-industry-news","category-left-hand"],"_links":{"self":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/4241","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\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/comments?post=4241"}],"version-history":[{"count":2,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/4241\/revisions"}],"predecessor-version":[{"id":4243,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/posts\/4241\/revisions\/4243"}],"wp:attachment":[{"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/media?parent=4241"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/categories?post=4241"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.predictiveanalyticsworld.com\/machinelearningtimes\/wp-json\/wp\/v2\/tags?post=4241"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}