Regression Metrics: MAE, RMSE and R²

Average error, big-error penalty, explained variance

MAE (mean absolute error) is the average size of errors in the target's units, easy to explain. RMSE (root mean squared error) also uses the target's units but punishes large errors more, useful when big misses are costly. R² compares the model with always predicting the mean: 1 is perfect, 0 is no better than the mean, negative is worse. Report a metric that matches the business cost of errors, in units people understand, and always next to a baseline.

Delivery time estimates

An app that is usually 2 minutes off but sometimes 40 minutes off may have a decent MAE and a poor RMSE; customers remember the 40-minute misses.

Explain errors in units

Say "on average 45 units off" rather than only quoting R2; stakeholders understand units.

त्वरित जाँच: What does an R² of 0 mean?

• Every prediction is zero
• The model is perfect
• The model is no better than always predicting the mean
• The data has no rows