PART 2 OF THE DEEP DIVE INTO THE ODDS SERIES
A complete guide on how to extract and explore odds ratios from a logistic regression model using Python and Statsmodels, with examples.
When we build a statistical model, we often focus more on its predictive value. However, we can also take advantage of it to discover the story behind the data.
Logistic regression is one of the simplest but most effective models for binary classification. Beyond prediction, we can obtain odds ratios for each variable in the fitted logistic regression model, which is invaluable to our understanding of the data.
In this article, as a continuation of the first article of the deep dive into the series of odds ratios, we will explore how to extract odds ratios from logistic regression. We will begin by deriving the relationship between the model and the odds ratios. Next, we will examine use cases where the logistic regression approach offers several advantages over the basic method of calculating odds ratios, including: calculating categorical and numerical variables, handling multiple variables, and addressing situations where variables have interaction effects.
