We never test the same attribute twice along one path in a decision tree. Why not?
Answer to relevant QuestionsSuppose we generate a training set from a decision tree and then apply decision-tree learning to that training set. Is it the case that the learning algorithm will eventually return the correct tree as the training set size ...Suppose that an attribute splits the set of examples E into subsets E i and that each subset has p, positive examples and n negative examples. Show that the attribute has strictly positive information gain unless the ratio ...Suppose one writes a logic program that carries out a resolution inference step. That is, let Resolve (c1, c2, c) succeed if c is the result of resolving cl and c2. Normally Resolve would be used as part of a theorem prover ...Consider the noisy-OR model for fever described in Section 14.3. Explain how to apply maximum-likelihood learning to fit the parameters of such a model to a set of complete data.Starting from Equation (20.13), show that δ I, / δ W j = Err x a j.
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