By Lior Rokach, Oded Maimon
This can be the 1st complete publication committed totally to the sector of choice bushes in facts mining and covers all elements of this crucial approach. determination bushes became probably the most robust and well known methods in wisdom discovery and knowledge mining, the technology and expertise of exploring huge and intricate our bodies of knowledge to be able to observe important styles. the world is of significant value since it permits modeling and data extraction from the abundance of knowledge to be had. either theoreticians and practitioners are regularly looking thoughts to make the method extra effective, good value and actual. choice timber, initially carried out in determination concept and statistics, are powerful instruments in different components reminiscent of info mining, textual content mining, details extraction, desktop studying, and trend recognition.This ebook invitations readers to discover the numerous advantages in information mining that call bushes provide: self-explanatory and simple to persist with while compacted; capable of deal with a number of enter information: nominal, numeric and textual; capable of procedure datasets which may have mistakes or lacking values; excessive predictive functionality for a comparatively small computational attempt; on hand in lots of info mining programs over a number of structures; and, important for varied initiatives, akin to class, regression, clustering and have choice.
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Additional resources for Data Mining with Decision Trees: Theory and Applications
Of course, in practice there is almost never one dominating model. The best answer that can be obtained is in regard to which areas one model outperforms the others. 6, every model gets diﬀerent values in diﬀerent areas. If a complete order of model performance is needed, another measure should be used. 8 1 Fig. 6 Areas of dominancy. A ROC curve is an example of a measure that gives areas of dominancy and not a complete order of the models. 2. 4. 9 to 1 again the dashed line model is the best. Area under the ROC curve (AUC) is a useful metric for classiﬁer performance since it is independent of the decision criterion selected and prior November 7, 2007 13:10 WSPC/Book Trim Size for 9in x 6in Evaluation of Classification Trees DataMining 35 probabilities.
2 A Test for the Diﬀerence of Two Proportions This statistical test is based on measuring the diﬀerence between the error rates of algorithms A and B [Snedecor and Cochran (1989)]. More speciﬁ- November 7, 2007 42 13:10 WSPC/Book Trim Size for 9in x 6in DataMining Data Mining with Decision Trees: Theory and Applications cally, let pA = (n00 + n01 )/n be the proportion of test examples incorrectly classiﬁed by algorithm A and let pB = (n00 + n10 )/n be the proportion of test examples incorrectly classiﬁed by algorithm B.
In this chapter we introduce the main concepts and quality criteria in decision trees evaluation. Evaluating the performance of a classiﬁcation tree is a fundamental aspect of machine learning. As stated above, the decision tree inducer receives a training set as input and constructs a classiﬁcation tree that can classify an unseen instance. Both the classiﬁcation tree and the inducer can be evaluated using evaluation criteria. The evaluation is important for understanding the quality of the classiﬁcation tree and for reﬁning parameters in the KDD iterative process While there are several criteria for evaluating the predictive performance of classiﬁcation trees, other criteria such as the computational complexity or the comprehensibility of the generated classiﬁer can be important as well.