Question: 1/50
A data scientist is asked to explain why a model’s performance drops when evaluated on new customer data compared to the training results. Which concept best describes this situation?
Overfitting due to learning noise and patterns specific to the training data
Underfitting due to using too many features in the model
Data leakage because the test set was never used during training
Gradient explosion caused by a large learning rate