{"id":383,"date":"2015-03-30T02:48:40","date_gmt":"2015-03-30T02:48:40","guid":{"rendered":"http:\/\/ec2-52-79-86-100.ap-northeast-2.compute.amazonaws.com\/?p=383"},"modified":"2024-05-01T14:36:01","modified_gmt":"2024-05-01T05:36:01","slug":"a-basic-tutorial-to-logistic-regression","status":"publish","type":"post","link":"https:\/\/blog.themusio.com\/?p=383","title":{"rendered":"A Basic Tutorial to Logistic Regression"},"content":{"rendered":"

For more info \u00a0:\u00a0log<\/a><\/p>\n

presenter : Jay<\/p>\n

The seminar covered the basics of logistic regression, which is the simplest method for classification. After going through an numeric example and the principle of maximum likelihood, an overview of logistic regression was given in relationship with other algorithms.The following was shown:
\n1. The equivalence between the Maximum Entropy algorithm and logistic regression
\n2. The equivalence between Naive bayes and logistic regression
\n3. How Conditional Random Fields are basically logistic regression with selective mapping of feature functions
\nConclusively, it was shown that logistic regression is a simple algorithm which works well when classifying linearly separable data with a reasonable running time.<\/p>\n","protected":false},"excerpt":{"rendered":"

For more info \u00a0:\u00a0log presenter : Jay The seminar covered the basics of logistic regression, which is the simpl […]<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_exactmetrics_skip_tracking":false,"_exactmetrics_sitenote_active":false,"_exactmetrics_sitenote_note":"","_exactmetrics_sitenote_category":0,"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[3642,3640],"tags":[4564,4172,3656,3700,4316,4174,4570,4366,4572,4146,4280,3710,4574],"class_list":["post-383","post","type-post","status-publish","format-standard","hentry","category-ai-en","category-all-en","tag-aka-ai-en","tag-algorithms-ja-en","tag-baggage-en","tag-cmos-en","tag-conditional-random-fields-en","tag-connection-en","tag-logistic-regression-en","tag-martin-ja-en","tag-maximum-entropy-en","tag-memory-en","tag-memory-neural-networks-en","tag-musio-en","tag-naive-bayes-en"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t