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The Decision Tree inductive learning algorithm may be used to generate "IF . THEN" rules that are consistent with a set of given examples.

The Decision Tree inductive learning algorithm may be used to generate “IF … THEN” rules that are consistent with a set of given examples. Consider an example where 10 binary input variables X1, X2, , X10 are used to classify a binary output variable (Y).(i) At most how many examples do we need to exhaustively enumerate every possible combination of inputs?(ii) At most how many leaf nodes can a decision tree have if it is consistent with a training set containing 100 examples?Please show detailed process how you obtain the solutions.

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