Overfitting and Underfitting in Neural Networks
Diagnose and address overfitting and underfitting through analysis of training versus validation performance.
Overfitting occurs when a model learns the training data too well, including its noise and quirks, resulting in poor generalization to unseen data. This happens with high-capacity models, insufficient regularization, or inadequate training data. Underfitting occurs when a model lacks capacity to capture underlying patterns, performing poorly on both training and validation sets. Diagnose overfitting by observing training loss decreasing while validation loss plateaus or increases. Diagnose underfitting when both losses remain high. Solutions to overfitting include regularization (L1/L2), dropout, early stopping, and data augmentation. Solutions to underfitting include increasing model capacity, training longer, or collecting more data.
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