DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Enterprise Data Mining Process Rattle Data Explore Building Models Evaluation and Deployment Moving into R Troubleshooting R 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

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. Bagging 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 bootstrap aggregating. See See Chapter 20

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