Question: What does the term \'overfitting\' refer to in the context of model selection?

Options:

1. A model that performs well on training data but poorly on unseen data
2. A model that is too simple to capture the underlying data patterns
3. A model that uses too many features
4. A model that is trained on too little data

Correct Answer: A model that performs well on training data but poorly on unseen data

Solution:

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern, leading to poor performance on unseen data.