2 docs tagged with "Metrics"

Why MAE, MSE and RMSE can report three different numbers for the same predictions, why RMSE is never below MAE, what their ratio tells you about outliers, and why scikit-learn hands back a negative mean squared error.

Settles why R² can exceed 1 or go negative, why SST = SSR + SSE only holds for a least-squares fit, why adding a junk feature never lowers R² but does lower Adjusted R², and why scikit-learn's MAPE returns 0.0296 rather than 2.96%.