From tan et al text Exercise 3.12. Learning objective is to show understanding of classifier performance analysis. Consider a labeled data set containing 100 data instances, which is randomly partitioned into two sets A and B, each containing 50 instances. We use A as the training set to learn two decision trees, T₁₀ with 10 leaf nodes and T₁₀₀ with 100 leaf nodes. The accuracies of the two decision trees on data sets A and B are show in the table below.

	Accuracy
Data Sets	T10	T100
A	0.86	0.97
B	0.84	0.77

Based on the accuracies shown above, which classification model you expect to have better performance on unseen instances?

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