What is Cook’s Distance and why is it important?

Cook’s Distance measures how much a single data point influences the regression model.

Cook’s Distance combines leverage (how far a point is in feature space) and residual (error size). A high value indicates that removing the point significantly changes the model coefficients. It is used to detect influential observations that distort learning and statistical inference.