MSE (Mean Squared Error)
Loss function measuring average squared difference between predicted and actual values.
In italiano: MSE (Mean Squared Error)MSE = (1/n) * Σ(y_true - y_pred)². Heavily penalizes large errors. Common for regression. Lower is better.
Examples
- Regression loss function
- Neural network training
- Error measurement