A: 0,1 B: 0,1 C: 0,1Show the decision tree ID3 learns from the training set of [A,B,C] examples:
[1,0,0]: positive [0,0,1]: positive [0,0,0]: negative [0,1,0]: negativeExplicitly show the gain of each feature at each choice point. If there is a tie in gain, prefer splitting on the feature first in the list: [A,B,C].
If represented as a logical rule, what is the function learned? Is it the same
or different from the function learned using decision trees?
S -> NP VP, NP -> Det N, NP -> Det Adj N, NP -> PN, NP -> NP PP, VP -> V, VP -> V NP, VP -> VP PP, PP -> Prep NP, Det -> the, Prep -> on, Prep -> in, Prep -> near, PN -> Israel, N -> bomber, N -> market, N -> suicide, N -> people, N -> street, V -> killed, V -> surrounding, Adj -> suicide, Adj -> surrounding,Do the obvious semantic interpretations of each of these various parse trees differ significantly from each other? If so, explain the interpretation of each parse tree and how it differs from that of the others. If not, explain why not.