Data & Analytics
MSE (Mean Squared Error)
Also known as:
Mean Squared Error
Quadratic Loss
L2 Loss
Updated: 2/12/2026
The average of squared differences between predicted and actual values – standard loss for regression.
Quick Summary
MSE is the standard regression loss – squares errors and heavily penalizes large deviations.
Explanation
MSE = 1/n × Σ(y_pred - y_true)². Squaring penalizes large errors disproportionately.
Marketing Relevance
MSE is the standard loss for regression – price prediction, demand forecasting, etc.
Common Pitfalls
Sensitive to outliers. Unit is squared. MAE is more robust.
Origin & History
MSE dates back to Gauss and Legendre (early 19th century) as part of the method of least squares.
Comparisons & Differences
MSE (Mean Squared Error) vs. MAE
MSE penalizes large errors more (quadratic); MAE treats all errors equally (linear).