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A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)

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How to understand if 2 very correlated predictors influence the output?

Typical approach is to use PCA and transform original features in uncorrelated components and select components that explain most of the variance. Also one of highly correlated features may be removed …
Milan Vukicevic's user avatar