logistic regression

Contributor(s): Ed Burns

Logistic regression is a statistical analysis method used to predict a data value based on prior observations of a data set. A logistic regression model predicts a dependent data variable by analyzing the relationship between one or more existing independent variables.

For example, a logistic regression could be used to predict whether a political candidate will win or lose an election or whether a high school student will be admitted to a particular college. The resulting analytical model can take into consideration multiple input criteria -- in the case of college acceptance, things such as the student's grade point average, SAT score and number of extracurricular activities. Based on historical data about earlier outcomes involving the same input criteria, it then scores new cases on their probability of falling into a particular outcome category.

Logistic regression has become an important tool in the discipline of machine learning. The approach allows an algorithm being used in a machine learning application to classify incoming data based on historical data. As more relevant data comes in, the algorithm should get better at predicting classifications within data sets. For that reason, logistic regression has become particularly popular in online advertising, enabling marketers to predict as a yes or no percentage the likelihood of specific website users who will click on particular advertisements.

Logistic regression can also play a role in data preparation activities by allowing data sets to be put into specifically predefined buckets during the extract, transform, load (ETL) process in order to stage the information for analysis.

This was last updated in June 2016

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What analytics use cases have you applied logistic regression to?


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