5. Avoiding Overfitting and Underfitting

One of the biggest challenges in model training is ensuring that the model generalizes well to unseen data. Two common issues that arise are:

• Overfitting: When a model learns the training data too well, capturing noise rather than general patterns.
• Underfitting: When a model is too simple and fails to capture the patterns in the data.

Techniques such as regularization, dropout, early stopping, and data augmentation can help mitigate these issues.