Loss Function
A mathematical function that measures the difference between predicted and actual values, guiding model training.
In more detail
The loss function quantifies prediction error. During training, the model minimizes loss to improve accuracy. Different tasks use different loss functions: cross-entropy for classification, MSE for regression, contrastive loss for metric learning.
Examples
- Cross-entropy loss for classification
- Mean Squared Error (MSE) for regression
- Contrastive loss for embeddings
- Focal loss for imbalanced data
Related terms
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