What are generalized linear models?

GLMs, or Generalized Linear Models, are a flexible class of statistical models used to describe the relationship between a set of predictor variables and a response variable. Unlike traditional linear regression, GLMs allow for non-normal distributions of the response variable and can model various types of data, such as binary, count, or continuous outcomes. Common examples include logistic regression for binary outcomes and Poisson regression for count data. GLMs consist of three components: a linear predictor, a link function, and a probability distribution from the exponential family. They are widely used in statistics, data analysis, and machine learning for analyzing complex datasets.