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Additional resources for Data Mining with Decision Trees: Theory and Applications
13). 2 illustrates the calculation of average Qrecall and average hitrate for a dataset of ten instances. The table presents a list of instances in descending order according to their predicted conditional probability to be classiﬁed as “positive”. Because all probabilities are unique, the third column (t[k] ) indicates the actual class (“1” represent “positive” and “0” represents “negative”). The average values are simple algebraic averages of the highlighted cells. 747 Note that both average Qrecall and average hit rate get the value 1 in an optimum classiﬁcation, where all the positive instances are located at the head of the list.
2 are identical and it obtains its lowest value when the two sets are mutually exclusive. Note that each point on the precision-recall curve may have a diﬀerent F-measure. Furthermore, diﬀerent classiﬁers have diﬀerent precision-recall graphs. November 7, 2007 13:10 WSPC/Book Trim Size for 9in x 6in Evaluation of Classification Trees Fig. 5 DataMining 27 A graphic explanation of the F-measure. Confusion Matrix The confusion matrix is used as an indication of the properties of a classiﬁcation (discriminant) rule.
November 7, 2007 13:10 20 WSPC/Book Trim Size for 9in x 6in Data Mining with Decision Trees: Theory and Applications TreeGrowing (S,A,y,SplitCriterion,StoppingCriterion) Where: S - Training Set A - Input Feature Set y - Target Feature SplitCriterion - the method for evaluating a certain split StoppingCriterion - the criteria to stop the growing process Create a new tree T with a single root node. IF StoppingCriterion(S) THEN Mark T as a leaf with the most common value of y in S as a label. ELSE ∀ai ∈ A find a that obtain the best SplitCriterion(ai , S).