{"id":2153,"date":"2021-11-01T04:28:01","date_gmt":"2021-11-01T04:28:01","guid":{"rendered":"http:\/\/optimumsportsperformance.com\/blog\/?p=2153"},"modified":"2021-11-01T17:59:11","modified_gmt":"2021-11-01T17:59:11","slug":"tidyx-81-tidymodels-logistic-regression","status":"publish","type":"post","link":"https:\/\/optimumsportsperformance.com\/blog\/tidyx-81-tidymodels-logistic-regression\/","title":{"rendered":"TidyX 81: tidymodels logistic regression"},"content":{"rendered":"<p>This week, <span style=\"color: #0000ff;\"><strong><a style=\"color: #0000ff;\" href=\"https:\/\/twitter.com\/ellis_hughes\">Ellis Hughes<\/a><\/strong><\/span> and I start exploring classification algorithms in {tidymodels}. We set up a logistic regression using NHL data to forecast whether a team will make the playoffs or not. Like all of our {tidymodels} episodes, we discuss:<\/p>\n<ol>\n<li>Initializing the model<\/li>\n<li>Splitting the data into training and test sets<\/li>\n<li>Creating cross-validation folds of the training data<\/li>\n<li>Setting up a model recipe<\/li>\n<li>Creating a model workflow<\/li>\n<li>Building and evaluating the model on the cross validation folds<\/li>\n<li>Fitting the model to the test data<\/li>\n<li>Evaluating the model predictions<\/li>\n<\/ol>\n<p>To view our screen cast, <strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" href=\"https:\/\/www.youtube.com\/watch?v=5wlmNSIyHP4\">CLICK HERE<\/a><\/span><\/strong>.<\/p>\n<p>To access our code, <strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" href=\"https:\/\/github.com\/thebioengineer\/TidyX\/tree\/master\/TidyTuesday_Explained\/081-Tidy_Models_5\">CLICK HERE<\/a><\/span><\/strong>.<\/p>\n<p>Also, if you enjoy our screen casts and find the useful, we have created a <strong><span style=\"color: #0000ff;\"><a style=\"color: #0000ff;\" href=\"https:\/\/www.patreon.com\/Tidy_Explained\">patreon page<\/a>. <\/span><\/strong><\/p>\n","protected":false},"excerpt":{"rendered":"<p>This week, Ellis Hughes and I start exploring classification algorithms in {tidymodels}. We set up a logistic regression using NHL data to forecast whether a team will make the playoffs or not. Like all of our {tidymodels} episodes, we discuss: Initializing the model Splitting the data into training and test sets Creating cross-validation folds of [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[44],"tags":[],"class_list":["post-2153","post","type-post","status-publish","format-standard","hentry","category-tidyx-screen-cast"],"_links":{"self":[{"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/posts\/2153","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/comments?post=2153"}],"version-history":[{"count":3,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/posts\/2153\/revisions"}],"predecessor-version":[{"id":2156,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/posts\/2153\/revisions\/2156"}],"wp:attachment":[{"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/media?parent=2153"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/categories?post=2153"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/optimumsportsperformance.com\/blog\/wp-json\/wp\/v2\/tags?post=2153"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}