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.