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 T ₁₀ with 10 leaf nodes and T ₁₀₀ with 100 leaf nodes. The accuracies of the two decision trees on data sets A and B are shown below:

(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 T ₁₀ and T ₁₀₀ on the entire dataset (A + B) and found that the classification accuracy of T ₁₀ on the data set (A + B) is 0.85, whereas the classification accuracy of T ₁₀₀ 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?

Data Set T10 0.86 T300 0.97 0.77 0.84