4. Consider the decision tree learning algorithm of Figure 7.7 and the data of Figure 7.1. Suppose, for this question, the stopping criterion is that all of the examples have the same classification.

The tree of Figure 7.6 was built by selecting a feature that gives the maximum information gain.

This question considers what happens when a different feature is selected.

(a) Suppose you change the algorithm to always select the first element of the list of features. What tree is found when the features are in the order [Author, Thread, Length, WhereRead]? Does this tree represent a different function than that found with the maximum information gain split? Explain.

(b) What tree is found when the features are in the order [WhereRead, Thread, Length

, Author]? Does this tree represent a different function than that found with the maximum information gain split or the one given for the preceding part? Explain.

(c) Is there a tree that correctly classifies the training examples but represents a different function than those found by the preceding algorithms? If so, give it. If not, explain why.