Data Mining with Decision Trees: Theory and Applications by Lior Rokach, Oded Maimon

By Lior Rokach, Oded Maimon

This is often the 1st finished publication devoted totally to the sector of selection timber in facts mining and covers all features of this crucial strategy. selection bushes became some of the most robust and renowned methods in wisdom discovery and knowledge mining, the technology and expertise of exploring huge and intricate our bodies of information in an effort to observe worthy styles. the world is of serious significance since it allows modeling and information extraction from the abundance of information to be had. either theoreticians and practitioners are constantly looking thoughts to make the method extra effective, budget friendly and exact. determination timber, initially applied in choice thought and information, are powerful instruments in different components resembling facts mining, textual content mining, details extraction, computing device studying, and trend recognition.This e-book invitations readers to discover the various advantages in information mining that call bushes provide: self-explanatory and simple to keep on with while compacted; in a position to deal with quite a few enter facts: nominal, numeric and textual; in a position to method datasets that can have error or lacking values; excessive predictive functionality for a comparatively small computational attempt; to be had in lots of facts mining applications over quite a few structures; and, necessary for varied initiatives, comparable to class, regression, clustering and have choice.

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The advantage of this approach is that each test set is independent of the others. However, there is the problem that the training sets overlap. This overlap may prevent this statistical test from obtaining a good estimation of the amount of variation that would be observed if each training set were completely independent of the others training sets. 3 13:10 WSPC/Book Trim Size for 9in x 6in Data Mining with Decision Trees: Theory and Applications Computational Complexity Another useful criterion for comparing inducers and classifiers is their computational complexity.

Each tuple is described by a vector of attribute values. The bag schema provides the description of the attributes and their domains. In this book, a bag schema is denoted as B(A∪y) where A denotes the set of input attributes containing n attributes: A = {a1 , . . , ai , . . , an } and y represents the class variable or the target attribute. Attributes (sometimes called field, variable or feature) are typically one of two types: nominal (values are members of an unordered set), or numeric (values are real numbers).

1997)] suggest using k = |dom(y)| / |S| and p = 1/ |dom(y)|. 2 No Match According to [Clark and Niblett (1989)] only zero probabilities are corrected and replaced by the following value: pa /|S|. [Kohavi et al. 5. They also empirically compared the Laplace correction and the no-match correction and indicate that there is no significant difference between them. However, both of them are significantly better than not performing any correction at all. 1 Algorithmic Framework for Decision Trees Decision tree inducers are algorithms that automatically construct a decision tree from a given dataset.

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