Loss Function
/lɒs ˈfʌŋkʃən/
A function that measures the difference between predicted and actual values, guiding model optimization.
Loss functions quantify model error. Common types include Mean Squared Error (MSE) for regression, Cross-Entropy for classification. Optimization minimizes loss through gradient descent.
Examples
- MSE for regression tasks
- Cross-entropy for classification
- Huber loss for robust regression