DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining with Rattle Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Summary

A good introduction is available from http://www.idiap.ch/~bengio/lectures/tex_ensemble.png

Bagging is bootstrap aggregation. The underlying idea is that part of the error due to variance in building a model comes from the specific choice of the training dataset. So create many similar training data sets, and for each of them train a new function. The final function will then be the average of each functions output.

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