regression-error-metrics

Judge whether a regression or forecast model's errors are good enough, and choose the error metric the business should see: MAE, RMSE, MAPE, WAPE, R-squared, bias, or a cost-weighted error. Use whenever someone asks whether an RMSE, MAE or MAPE value is good; why R-squared is high but predictions are still useless, or negative on test data; which metric to report for price, demand, delivery-time, sales or salary predictions; why MAPE explodes when actual values are small; how to compare a model with a naive, last-period or average baseline; how errors differ across segments or ranges; whether over- and under-prediction cost the same; or how to read a residual plot for a predictive model. Not for classification metrics or thresholds, not for ranking or recommender metrics, and not for testing whether a coefficient is statistically significant.