DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Conditional Trees Decision Trees Hierarchical Clustering Kernel Methods K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products Borgelt Data Mining Suite R Rattle Weka Closed Products C4.5 Clementine Enterprise Miner Equbits Foresight GhostMiner InductionEngine ODM Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Resources and Further Reading

The traditional decision tree algorithms suffer from overfitting and a bias toward selecting variables with many possible splits. The algorithms do not use any statistical significance concepts and thus, as noted by (), cannot distinguish between significant and insignificant improvements in the information measure. Conditional Trees (See Chapter ) take into account distributional properties.

Can library(rgl) be used to visualise a decision tree?

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