É um assunto batido, mas sempre vale uma rápida revisão.

Um trecho: “[…]To generalize, a model that overfits its training set has low bias but high variance – it predicts the targets in the training set very accurately, but any slight changes to the predictors would result in vastly different predictions for the targets. Overfitting differs from multicollinearity, which I will explain in later post. Overfitting has irrelevant predictors, whereas multicollinearity has redundant predictors. […]”