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

Two Class Models

This chapter focuses on the common data mining task of binary (or two class) classification. This is the task of distinguishing between two classes of entities - whether they be high risk and low risk insurance clients, productive and unproductive audits, responsive and non-responsive customers, successful and unsuccessful security breaches, and many other similar examples.

Rattle provides a straight-forward interface to the common collection of model builders used in data mining. For each, a basic collection of the commonly used tuning parameters is exposed through the interface for fine tuning the model performance. Where possible, Rattle attempts to present good default values to allow the user to simply build a model with no or little tuning. This may not always be the right approach, but is certainly a good place to start.

The model builders provided by are: Decision Trees, Boosted Decision Trees, Random Forests, Support Vector Machines, and Logistic Regression.

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