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

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()

http://rattle.togaware.com/code/rplot-rocr-survey-lift.R

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