3.4. ID3 searches for just one consistent hypothesis, whereas the CandDATE- ELIMINATIon algorithm finds all consistent hypotheses. Consider the correspondence between these two learning algorithms (a) Show the decision tree that would be learned by ID3 assuming it is given the four training examples for the EnjoySport? target concept shown in Table 2.1 of Chapter 2. (b) What is the relationship between the learned decision tree and the version space (shown in Figure 2.3 of Chapter 2) that is learned from these same examples? Is the learned tree equivalent to one of the members of the version space? (c) Add the following training example, and compute the new decision tree. This time, show the value of the information gain for each candidate attribute at each step in growing the tree. Sky Air-Temp Humidity Wind Water Forecast Enjoy-Sport? SunnyWarm Normal Weak Warm Same 0 (d) Suppose we wish to design a learner that (like ID3) searches a space of decision tree hypotheses and (like CaNDIDATE-ELIMINATIoN) finds all hypotheses con- sistent with the data. In short, we wish to apply the CANDIDATE-ELIMINATION algorithm to searching the space of decision tree hypotheses. Show the S and G sets that result from the first training example from Table 2.1. Note S must contain the most specific decision trees consistent with the data, whereas G must contain the most general. Show how the S and G sets are refined by the.second training example (you may omit syntactically distinct trees that describe the same concept). What difficulties do you foresee in applying CandidATE-ELimiNATION to a decision tree hypothesis space