Glossary · Phase 3 · Machine learning
MAPE
The average size of the errors as a percentage of the actual values; easy to read, but undefined when an actual value is zero.
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Squared error punishes one big miss far more than several small ones. That's why a single outlier can drag a squared-error model.
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Loss functions · One number that says how wrong the model is. About 25 minutes.
Other terms from the same lesson
- Huber loss: A loss that treats small misses like squared error and big misses like absolute error, so a few wild values can't dominate training.
- loss: A single number saying how wrong a model's predictions are. Training tries to make it small.
- mean absolute error: The average size of the misses, ignoring whether each was too high or too low. A miss twice as big costs twice as much.
- mean squared error: A loss: square each prediction's error and average them, so big mistakes count extra.
- quantile loss: A lopsided loss that charges more for misses in one direction, such as running out of stock costing more than overstocking.
- RMSE: The square root of the mean squared error: an error in the target's own units that still punishes big misses more than small ones.
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