Since the rules are extracted by following independent paths directly from the tree, this guarantees such properties, as for mutually exclusiveness, that happens as we have one specific antecedent combination per leaf (that represent the unique path), so no identical rules will be triggered for the same tuple. And as for exhaustive property, there is one rule for each possible attribute–value split combination, so that this set of rules does not require the default catch all rule.Since we end up with one rule per leaf, the set of extracted rules is not much simpler than the corresponding decision tree! The extracted rules may be even more difficult to  interpret  than  the  original  trees  in  some  cases.  As  an  example,  Figure  8.7  showed decision trees that suffer from subtree repetition and replication. The resulting set of rules extracted can be large and difficult to follow, because some of the attribute tests may be irrelevant or redundant. So, the plot thickens. Although it is easy to extract rules from  a  decision  tree,  we  may  need  to  do  some  more  work  by  pruning  the  resulting rule setAlso explain and discuss the impact of rule simplification on these properties.Rule simplification affects the properties of the classifier as it neglects the importance of checking the splits conditions that happens through the induction process, which meets by definition the mutually exclusive and exhaustive properties by enabling each leaf to be covered by one rule, and this becomes invalid when simplifying the rule, the unique antecedent will be redundant in connection to multiple leafs (for the mutual exclusive property case) and some example might lose the rule that cover them, i.e. there won’t be that one rule for each possible attribute–value combination, and in that sense, the set of rules will require a default catch all rule.