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

Bagging:
Meta Algorithm

Bagging is a variance reduction method for model building. That is, through building multiple models from samples of the training data, the aim is to reduce the variance. http://en.wikipedia.org/wiki/BaggingBagging is a technique generating multiple training sets by sampling with replacement from the available training data. In an ideal world we can eliminate variance due to a particular choice of training set by combining models that are built from each training set of size N. In practise only one training set is available. By sampling with replacement from the training set to form new training sets, bagging simulates the ideal situation. Bagging is also known as http://en.wikipedia.org/wiki/Bootstrap_Aggregatingbootstrap aggregating. See See Chapter 

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