Generalized Linear Models: Practical Applications in Data Science
Generalized Linear Models (GLMs) are a powerful yet often underutilized tool in the data scientist’s arsenal. While many data practitioners are comfortable with linear and logistic regression, GLMs offer a flexible framework for modeling a variety of real-world problems, especially when the target variable doesn’t fit neatly into the assumptions required by standard regression models. In this article, we’ll explore two practical and high-impact applications of GLMs: the Poisson GLM for count data and the Gamma GLM for modeling positive, skewed outcomes. We’ll then show how combining these approaches unlocks even greater predictive power, particularly in industries like insurance and retail.
A Generalized Linear Model (GLM) extends traditional linear regression by allowing the response variable to have a non-normal distribution and by using a link function to relate the expected value of the response variable to the linear predictors. This flexibility makes GLMs suitable for a wide range of tasks beyond what ordinary least squares regression can handle.
The general form of a GLM is:
