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

Examples

A number of R packages implement boosting. The caTools package provides the LogitBoost function which is perhaps the simplest to use, and is an efficient implementation for large datasets. The boost package provides the adaboost function as well as logitboost, and relies on rpart for building the models, and is less efficient. The gbm package is the more sophisticated of the packages and implements the more general Generalise Boosted Regression Models. We will illustrate boosting with the gbm package.

We start our examples though with a step through of the process using just rpart.

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