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Extracting rules from a random forest. Frequency of a rule is defined as the proportion of data instances satisfying the rule condition. The frequency measures the popularity of the rule. Error of a rule is defined as the number of incorrectly classified instances determined by the rule. So she is able to say that for 80% of the customers with 100% accuracy (ie. 0% error), when income >20 k and there are zero missed payments, the application is approved.
