What does $$overfitting$$ in a decision tree model imply?

What does $$overfitting$$ in a decision tree model imply?

The model is too complex, capturing noise in the training data as if it were a real pattern.

The model is too generalized, resulting in poor training data performance.

The model performs poorly on both training and testing data.

The model performs exceptionally well on testing data but poorly on training data.