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

Evaluating Models

Evaluating the outcomes of data mining is important. We need to understand how well any model we build will be expected to perform, and how well it performs in comparison to other models we might choose to build.

A common approach is to compute an error rate which simply reports the number of cases that the model correctly classifiers. Common methods for estimating the empirical error rate are, for example, cross-validation (CV), the Bayesian evidence framework, and the PAC framework.

In this chapter we introduce several measures used to report on the performance of a model and review various approaches to evaluating the output of data mining. This will cover printcp, table for producing confusion matrices, ROCR for the graphical presentation of evaluations, as well as how to tune the presentations for your own needs.

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