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

Lift

pdf("graphics/rplot-rocr-survey-lift.pdf")
library(rpart)
library(ROCR)
load("survey.Rdata")
survey.rp <- rpart(Salary.Group ~ ., data=survey)
survey.pred <- predict(survey.rp, data=survey)
pred <- prediction(survey.pred[,2], survey$Salary.Group)
lift <- performance(pred, "lift", "rpp")
plot(lift)
dev.off()

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