Mean Squared Error (MSE) Calculator
Local ProcessingCalculate average squared difference MSE = (1/n) × Σ (Actual - Predicted)² between values.
How It Works & Processing Standards
Enter values above to calculate results automatically. Check each input field for guidance and units.
Example: Actual Ground Truth Values = [1, 2, 3], Model Predicted Values = [1, 3, 5] → 1.666667
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MSE = Σ(actual − predicted)² ÷ n. Squaring penalizes large errors more.
How to Use Mean Squared Error (MSE) Calculator
- 1Enter actual valuesThe observed values.
- 2Enter predictionsSame order and same length.
- 3Read the MSECloser to 0 is more accurate. Its square root is the RMSE.
Examples
Four predictions
Input: Actual 3, -0.5, 2, 7 / predicted 2.5, 0, 2, 8
Result: 0.375
Three predictions
Input: Actual 10, 20, 30 / predicted 12, 18, 33
Result: 5.66666666667
Common Use Cases
- Comparing two forecasting models
- Demand forecast versus actual sales
- Checking machine learning homework
Frequently Asked Questions (FAQ)
How do I get the RMSE?
Take the square root of the MSE. An MSE of 0.375 gives an RMSE of about 0.612, in the original units.
How is it different from MAE?
MAE averages absolute errors. MSE squares them, so large errors count more.