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

Decision TreeThe Decision Tree inductive learning algorithm may be used to generate “IF … THEN” rules that are consistent with a set of given examples. X1, X2, , X10 are used to classify a binary output variable (Y).Please show detailed process how you obtain the solutions.At most how many leaf nodes can a decision tree have if it is consistent with a training set containing 100 examples?So far I have:With the ten Boolean attributes for n, it gives us or 21024 different functions to choose from, that is a huge number. Since that would mean as many possible leafs since a leaf specifies the value to be returned by a function.In this scenario we have no way of observing an attribute that will distinguish the examples. Therefore, split the total number of examples evenly into positive and negative examples since no matter what attribute you choose, they are all binary.I = p + n, I = 100, p = 50, n = 50I am fairly sure there is some sort of function, probably a recursive one, for accurately determining this. I just have no idea what it is nor where to find it.