Consider a labeled data set containing 100 data instances which are randomly partitioned into two sets A and B, each containing 50 instances. We use A as the training set to learn two decision trees T10with 10 leaf nodes and T100 with 100 leaf nodes. The accuracies of the two decision trees on data sets A and B are shown below

Data set T10 T100

A 0.86 0.97

B 0.81 0.77

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

(b) Now you've tested T10 and T100 on the entire dataset (A + B) and found that the classification accuracy of T10 on the data set (A + B) is 0.85, whereas the classification accuracy of T100 on the data set (A + B) is 0.87. Based on this new information and your observations from the table above, which classification model would you finally choose for classification?