Question: Explain the concept of overfitting in machine learning.

Answer: Overfitting occurs when a model learns the training data too well, capturing noise and outliers instead of general patterns. This can lead to poor performance on new, unseen data.

Example:

A complex polynomial regression model fitting the training data perfectly but performing poorly on test data.