DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface List of Figures List of Tables Process . 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

Imbalanced Decisions

Model accuracy is not such an appropriate measure of performance when the data has a very imbalanced distribution of outcomes. For example, if positive cases account for just 1% of all cases, as might be the case in an insurance dataset recording cases of fraud, then the most accurate, but most useless, of models is one that predicts no fraud in all cases. It will be 99% accurate!

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