by Michele Laurelli
Loss function measuring average squared difference between predicted and actual values.
MSE = (1/n) * Σ(y_true - y_pred)². Heavily penalizes large errors. Common for regression. Lower is better.
Regression loss function
Neural network training
Error measurement
A function that measures the difference between predicted and actual values, guiding model optimization.
A supervised learning task where the goal is to predict continuous numerical values.
Loss function measuring average absolute difference between predicted and actual values.