How can model overfitting be avoided during training?

Using regularization techniques is a highly effective method for avoiding model overfitting during training. Overfitting occurs when a model learns not only the underlying patterns in the training data but also the noise and outliers, resulting in poor generalization to unseen data.

Regularization techniques introduce a penalty for increased complexity in the model, discouraging it from fitting to the noise. Common examples of regularization include L1 and L2 regularization, which add a constraint on the model parameters, and dropout, which randomly disables a portion of the neurons during training to prevent co-adaptation.

This approach helps the model to remain simpler and more generalizable, therefore improving its performance on validation and test datasets. Regularization effectively balances the trade-off between fitting the training data well while still maintaining the ability to generalize to new, unseen instances.