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Loss Function
Data / Training / Evaluation
Measures model error during training.
Loss functions quantify how far model predictions deviate from true labels. The training process minimizes this error.
- Examples: Mean Squared Error (MSE), Cross-Entropy, Hinge Loss.
- Importance: The choice of loss influences convergence and final accuracy.