What are some of the most common causes of overfitting in machine learning models, and how do you mitigate them?

Overfitting typically occurs when a model is too complex, trained for too many epochs, or when there’s insufficient data. To mitigate this, I would use techniques like regularization (L1, L2), dropout in neural networks, early stopping, and data augmentation.For example, Using early stopping in a neural network can prevent overfitting by halting training when performance on the validation set starts to deteriorate.