Loss Functions, Training Loss, Validation Loss
Loss functions measure how far model predictions are from the correct outputs. They guide the training process. Lower loss means better performance.
What Is a Loss Function
A loss function gives the model a numeric score. This score shows how wrong the prediction is. Training tries to reduce this score.
Common Loss Functions
1. Mean Squared Error
Used in regression. Measures squared distance between predicted and true values.
2. Cross Entropy Loss
Used in classification. Measures how well predicted probabilities match true labels.
3. Hinge Loss
Used in SVM tasks. Pushes correct classes away from the margin.
4. MAE
Mean Absolute Error. Measures absolute difference between prediction and truth.
What Is Training Loss
Training loss is the loss measured on the training dataset. It shows how well the model learns from the data it sees during training.
What Is Validation Loss
Validation loss is the loss measured on unseen data. This data is not used for training. It checks if the model generalizes well.
Why Training and Validation Loss Matter
- Training loss shows learning progress.
- Validation loss shows generalization.
- Low training loss with high validation loss means overfitting.
- High training and validation loss means underfitting.
How To Use Loss Curves
- Track training loss each epoch.
- Track validation loss each epoch.
- Stop training when validation loss stops improving.
- Adjust model size or regularization based on trends.
Loss Functions in Moroccan Darija
Loss function hiyya score kat9is sh7al prediction b3id 3la truth. Training y7awel y9allel had score.
Training Loss
Loss f data li model kayt3llam biha.
Validation Loss
Loss f data li model ma chafhach. Katban wach model kay3mmem mzyan.
Nqat Sahl
- Ila training loss hbat w validation loss tla, 3ndna overfitting.
- Ila juj high, 3ndna underfitting.
Conclusion
Loss functions measure model errors. Training loss tracks learning. Validation loss tracks generalization. Strong models keep both losses low and stable.