Page 104 - Demo

%u062c%u0645%u064a%u0639 %u0627%u0644%u062d%u0642%u0648%u0642 %u0645%u062d%u0641%u0648%u0638%u0629 %u0640 %u0627%u0625%u0644%u0639%u062a%u062f%u0627%u0621 %u0639%u0649%u0644 %u062d%u0642 %u0627%u0645%u0644%u0624%u0644%u0641 %u0628%u0627%u0644%u0646%u0633%u062e %u0623%u0648 %u0627%u0644%u0637%u0628%u0627%u0639%u0629 %u064a%u0639%u0631%u0636 %u0641%u0627%u0639%u0644%u0647 %u0644%u0644%u0645%u0633%u0627%u0626%u0644%u0629 %u0627%u0644%u0642%u0627%u0646%u0648%u0646%u064a%u0629104rule%u2019s consequent contains a class prediction (in this case, we are predicting whether a customer will buy a computer). R1 can also be written as R1: (age = youth) %u2227 (student = yes) %u21d2 (buys computer = yes). If the condition (i.e., all the attribute tests) in a rule antecedent holds true for a given tuple, we say that the rule antecedent is satisfied (or simply, that the rule is satisfied) and that the rule covers the tuple. A rule R can be assessed by its coverage and accuracy. Given a tuple, X, from a class-labeled data set, D, let ncovers be the number of tuples covered by R; n correct be the number of tuples correctly classified by R; and |D| be the number of tuples in D. We can define the coverage and accuracy of R as coverage(R) = ncovers /|D| /* D: training data set */ accuracy(R) = ncorrect / ncovers If more than one rule are triggered, need conflict resolution. Size ordering: assign the highest priority to the triggering rules that has the %u201ctoughest%u201d requirement (i.e., with the most attribute tests).Class-based ordering: decreasing order of prevalence or misclassification cost per class.Rule-based ordering (decision list): rules are organized into one long priority list, according to some measure of rule quality or by experts. Rule Extraction from a Decision Tree: To extract rules from a decision tree, one rule is created for each path from the root to a leaf node. Each splitting criterion along a given path is logically ANDed to form the rule antecedent (%u201cIF%u201d part). The leaf node holds the class prediction, forming the rule consequent (%u201cTHEN%u201d part). We use this approach because: Rules are easier to understand than large trees, One rule is created for each path from the root to a leaf, each attribute-value pair along a path forms a conjunction: the leaf holds the class prediction ,rules are mutually exclusive and exhaustive. Example: