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Decision Tree Induction

Decision Tree Induction

Roberta Siciliano, Claudio Conversano
Copyright: © 2009 |Pages: 7
ISBN13: 9781605660103|ISBN10: 1605660108|EISBN13: 9781605660110
DOI: 10.4018/978-1-60566-010-3.ch098
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MLA

Siciliano, Roberta, and Claudio Conversano. "Decision Tree Induction." Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, IGI Global, 2009, pp. 624-630. https://doi.org/10.4018/978-1-60566-010-3.ch098

APA

Siciliano, R. & Conversano, C. (2009). Decision Tree Induction. In J. Wang (Ed.), Encyclopedia of Data Warehousing and Mining, Second Edition (pp. 624-630). IGI Global. https://doi.org/10.4018/978-1-60566-010-3.ch098

Chicago

Siciliano, Roberta, and Claudio Conversano. "Decision Tree Induction." In Encyclopedia of Data Warehousing and Mining, Second Edition, edited by John Wang, 624-630. Hershey, PA: IGI Global, 2009. https://doi.org/10.4018/978-1-60566-010-3.ch098

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Abstract

Decision Tree Induction (DTI) is a tool to induce a classification or regression model from (usually large) datasets characterized by n objects (records), each one containing a set x of numerical or nominal attributes, and a special feature y designed as its outcome. Statisticians use the terms “predictors” to identify attributes and “response variable” for the outcome. DTI builds a model that summarizes the underlying relationships between x and y. Actually, two kinds of model can be estimated using decision trees: classification trees if y is nominal, and regression trees if y is numerical. Hereinafter we refer to classification trees to show the main features of DTI. For a detailed insight into the characteristics of regression trees see Hastie et al. (2001). As an example of classification tree, let us consider a sample of patients with prostate cancer on which data Figure 1. The prostate cancer dataset such as those summarized in Figure 1 have been collected. Suppose a new patient is observed and we want to determine if the tumor has penetrated the prostatic capsule on the basis of the other available information. Posing a series of questions about the characteristic of the patient can help to predict the tumor’s penetration. DTI proceeds in such a way, inducing a series of follow- up (usually binary) questions about the attributes of an unknown instance until a conclusion about what is its most likely class label is reached. Questions and their alternative answers can be represented hierarchically in the form of a decision tree, such as the one depicted in Figure 2.

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