1.Using the concept of overfitting, explain why when a model is fit to training data, zero error with those data is not necessarily good.

2. Two models are applied to a dataset that has been partitioned. Model A is considerably more accurate than model B on the training data, but slightly less accurate than model B on the validation data. Which model are you more likely to consider for final deployment?