Mean Squared Error (MSE) Calculator
Enter actual and predicted values to evaluate average squared error and root mean squared error (RMSE).
MSE
1.6250
RMSE
1.2748
How to Use This Calculator
- Enter actual values and corresponding predictions in the two input boxes.
- Ensure both lists have equal length and numeric entries.
- Review the resulting MSE and RMSE.
- Use MSE to evaluate model performance or forecasting accuracy.
Formula
MSE = Σ (yi − ŷi)² / n
RMSE = √MSE
MSE penalizes large errors more heavily due to squaring. RMSE restores the original units for easier interpretation.
Full Description
Mean squared error quantifies average squared deviation between observed and predicted values, making it a key metric in regression, machine learning, and forecasting. RMSE, the square root, preserves the original units and provides a more interpretable error magnitude.
Frequently Asked Questions
What is a good MSE value?
Lower values indicate better fit. Compare MSE relative to target variance or benchmark models.
Should I use RMSE or MAE?
RMSE emphasizes larger errors; MAE treats all errors linearly. Choose based on sensitivity requirements.
Do actual and predicted lists need the same length?
Yes. Mismatched lengths invalidate the calculation.
Can I include negative values?
Yes. The formula squares differences, so sign does not affect magnitude.