Linear Regression (page 3 of 7)

All machine learning algorithms minimize or maximize an “objective function”. Each observation's loss determines how good (or bad) a predictive model predicts its known continuous target. The most common way to calculate losses is L1 (least absolute errors) and L2 (least squared errors). A model's cost function is the average of the losses across all observations.