DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Decision Trees

Decision trees are one of the classic machine learning techniques widely deployed in data mining.

The model (i.e., the discovered knowledge) is expressed in the form of a simple decision tree. At each node of the tree we test the value of one of the variables, and depending on its value, we follow one of the branches emanating from that node. Thus, each branch can be thought of as having a test associated with it, for example Age $<$ 35. This branch then leads to another node where there will be another variable to test, and so on, until we reach a leaf node of the tree. The leaf node represents the decision to be made. For example, it may be a yes or no for deciding whether an insurance claim appears to be fraudulent.

The algorithm for searching through all possible decision trees

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