You are given a classification dataset with $100$ instances, which has been

partitioned into two subsets, a dataset A with $50$ instances and a dataset B

with $50$ instances. Dataset A is used for training and dataset B is used for

testing. You are supposed to compare two classification models: Model $1,$

which is an unpruned decision tree, and Model $2,$ which is a pruned version of

the decision tree. The accuracy of the two classification models on datasets

A and B are shown in the table below.

Classification Dataset A Dataset B

$($a$)$ Based on the accuracies shown in the table above, which classification model would you expect to have better performance on unseen

instances? Support your answer with a brief explanation.