DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Enterprise Data Mining Process Rattle 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

Sample Tab

Here we specify how we might partition the dataset for exploratory and modelling purposes. The default for Rattle is to build two subsets of the dataset: one is a training set from which to build models, while the other is used for testing the performance of the model. The default for Rattle is to use a 70% training and a 30% testing split, but you are welcome to turn sampling off, or choose other samplings. A very small sampling may be required to perform some explorations of the smaller dataset, or to build models using the more computationally expensive algorithms (like support vector machines).

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