Shobha
The best way to predict the future is to invent it
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.