Calculate Mean Squared Error (MSE) by Hand
Introduction & Importance
Mean Squared Error (MSE) is a common metric used to evaluate the performance of an estimator by comparing the predicted values to the actual values. Calculating MSE by hand is crucial for understanding the underlying math and for scenarios where computational resources are limited.
How to Use This Calculator
- Enter the y values (actual values) separated by commas in the ‘y values’ field.
- Enter the y hat values (predicted values) separated by commas in the ‘y hat values’ field.
- Click the ‘Calculate’ button.
Formula & Methodology
The formula for MSE is:
MSE = [(1/n) * ∑(yi – ŷi)²]
Where:
- n is the number of observations.
- yi is the actual value.
- ŷi is the predicted value.
Real-World Examples
Data & Statistics
| Algorithm | MSE |
|---|---|
| Linear Regression | 0.05 |
| Decision Tree | 0.03 |
| Random Forest | 0.02 |
Expert Tips
- MSE is sensitive to outliers and is always non-negative.
- Lower MSE values indicate better fit.
- MSE is not interpretable in its raw form. It’s often reported in terms of the raw unit squared.
Interactive FAQ
What is the difference between MSE and RMSE?
Root Mean Squared Error (RMSE) is the square root of the MSE. RMSE is in the same units as the original data, making it easier to interpret.
For more information, see the Kaggle guide on metrics.