AI engineering glossary · Evaluation metrics
What is MSE (mean squared error)?
Mean squared error (also called MSE): The average of the squared differences between predictions and true values: MSE = (1/N) * sum of (y - y_hat)^2. Squaring makes all errors positive and punishes large errors far more than small ones. It is the standard training loss for regression, but its units are the square of the target's units.
Mean squared error: an example
For actual prices [50, 60, 70, 80] lakh and predictions [55, 58, 72, 100], the squared errors are 25, 4, 4, 400, so MSE = 108.25.
Related terms
More Evaluation metrics terms
- Mean squared logarithmic error
- Median absolute error
- METEOR
- Micro averaging
- Negative predictive value
- Normalised discounted cumulative gain
- Normalised mutual information
- Normalised RMSE
Where you learn it
Mean squared error is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.