Can you explain overfitting and how to prevent it in PyBrain?
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and irrelevant patterns in addition to the underlying relationships between input and output variables.
As a result, the model performs well on the training data but poorly on unseen data, indicating poor generalization.
In PyBrain, overfitting can occur when training neural networks with overly complex architectures or insufficient regularization.
