linear-and-logistic-models

Build, regularise and interpret linear regression, logistic regression and multinomial (softmax) models used for prediction. Always use this skill when someone asks what a regression coefficient or an odds ratio means, or why a coefficient changed size or flipped sign when another feature was added, even when the question sounds like a quick one. Also use it for standardising features before comparing coefficients; dummy variables and the dropped category; multicollinearity or VIF; Ridge vs Lasso vs Elastic Net and choosing alpha; polynomial or interaction terms; logistic regression vs a perceptron vs linear discriminant analysis; gradient descent that will not converge; or whether a linear model is enough. Not for deciding whether X causes Y or whether an experiment's result is significant, not for choosing a regression error metric, and not for explaining what these terms mean when no model or data is in play.