Generalized Linear Models (GLMs) provide a unified mathematical framework that combines linear regression and logistic regression by using probability distributions from the exponential family; for binary classification, the Bernoulli distribution naturally produces the sigmoid function, while continuous values use the Gaussian distribution, enabling predictions through the integration of probability distributions with linear models.
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What if linear regression and logistic regression were part of the same framework? GLM's unify regression and classification using probability distributions. GLM's are built using distributions from the exponential family. Different probability distributions fit into the same mathematical structure. For binary classification, we use the Bernoulli distribution. The sigmoid function naturally appears from the Bernoulli distribution. For continuous values, we use the Gaussian distribution. GLM's combine probability distributions with linear models to make predictions.
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