by Michele Laurelli
Loss function measuring average absolute difference between predicted and actual values.
MAE = (1/n) * Σ|y_true - y_pred|. More robust to outliers than MSE. Linear penalty for errors.
Robust regression loss
Outlier-resistant evaluation
Error magnitude
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 squared difference between predicted and actual values.