Decision Tree]
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.
Bayesian Belief Networks
A quality control manager has used algorithm C4.5 to come up with rules that classify items based on several input factors. The output has two classes -- Accept and Reject. Test results with the rule set indicate that 5% of the good items are classified as
Reject and 2% of the bad items classified as Accept.
Historical data suggests that one percent of the items are bad. Based on this information, what is the conditional probability that:
(i) An item classified as Reject is actually good? (ii) An item classified as Accept is actually bad?
Please show detailed process how you obtain the solutions.