e$)$ regularization to penalize large coefficients.

$3.$ Pruning: In decision trees, remove branches that have little importance.

$4.$ Cross$-$Validation: Use techniques like k$-$fold cross$-$vali

$5.$ Early Stopping: Monitor thodified versions of existing data.

$7.$ Dropout: In neural networks, randomly drop units during training to prevent co$-$adaptation.

Underfitting:

$$ Definition: Underfitting occurs when a model is too simple to capture the underlying pattern of the data. It fails to perform well on both the training and validation$/$test data.

$$ Criteria:

o Low accuracy on both training and validation$/$test data.