Generalized Linear Model

Generalized Linear Model (GLM) is a flexible framework for modeling a wide range of data types, including continuous, binary, count, and categorical data. It is an extension of the linear regression model that allows for non-normal error distributions and non-linear relationships between the response and predictor variables. GLM consists of three components: a probability distribution from the exponential family, a linear predictor function, and a link function that connects the two. The model is fit using maximum likelihood estimation, and the goodness of fit is assessed using deviance and residual analysis. GLM has many applications in data science, including in finance, healthcare, and social sciences.
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