{"id":1915,"date":"2019-03-23T06:33:03","date_gmt":"2019-03-23T06:33:03","guid":{"rendered":"https:\/\/www.aiproblog.com\/index.php\/2019\/03\/23\/logistic-regression-in-one-picture\/"},"modified":"2019-03-23T06:33:03","modified_gmt":"2019-03-23T06:33:03","slug":"logistic-regression-in-one-picture","status":"publish","type":"post","link":"https:\/\/www.aiproblog.com\/index.php\/2019\/03\/23\/logistic-regression-in-one-picture\/","title":{"rendered":"Logistic Regression in One Picture"},"content":{"rendered":"

Author: Stephanie Glen<\/p>\n

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Logistic regression is regressing data to a line (i.e. finding an average of sorts) so you can fit data to a particular equation and make predictions for your data. This type of regression is\u00a0a good choice when modeling binary variables<\/a>, which happen frequently in real life (e.g. work or don’t work, marry or don’t marry, buy a house or rent…). The logistic regression model is popular, in part, because it gives probabilities between 0 and 1. Let’s say you were modeling a risk of credit default: values closer to 0 indicate a tiny risk, while values closer to 1 mean a very high risk. The following image shows an example of how one might tailor a logistic model for credit score based risk.<\/p>\n

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Click on the picture to zoom in<\/em><\/p>\n

References<\/h2>\n

Hilbe, J. (2016). Practical Guide to Logistic Regression. CRC Press.<\/p>\n

DSC Resources<\/strong><\/span><\/p>\n