Conditional inference trees (ctree) in package `party` allows weighting which is useful when one classification outcome is more important than another. Useful examples are not difficult to imagine: in a marketing direct mailing, a false positive (non-response) costs just paper and postage (say, $0.50) while a true positive (response) may be worth $100.00. In a medical diagnostic test, a false negative screening for cancer could be fatal.

The following code demonstrates how to use weighting with ctree in party in R 2.10.

# load the mlbench package which has the BreastCancer data set require(mlbench) # if you don't have any required package, use the install.packages() command # load the data set data(BreastCancer) # remove the unique identifier, which is useless and would confuse the machine learning algorithms BreastCancer$Id <- NULL # partition the data set for 80% training and 20% evaluation (adapted from ?randomForest) set.seed(2) ind <- sample(2, nrow(BreastCancer), replace = TRUE, prob=c(0.8, 0.2)) # model using ctree without weights require(party) x.ct <- ctree(Class ~ ., data=BreastCancer[ind == 1,]) x.ct.pred <- predict(x.ct, newdata=BreastCancer[ind == 2,]) x.ct.prob <- 1- unlist(treeresponse(x.ct, BreastCancer[ind == 2,]), use.names=F)[seq(1,nrow(BreastCancer[ind == 2,])*2,2)] # model using ctree with weights 1:10 (benign:malignant) x.ctw <- ctree(Class ~ ., data=BreastCancer[ind == 1,], weights= ifelse(BreastCancer[ind == 1,]$Class=='benign', 1, 10)) x.ctw.pred <- predict(x.ctw, newdata=BreastCancer[ind == 2,]) x.ctw.prob <- 1- unlist(treeresponse(x.ctw, BreastCancer[ind == 2,]), use.names=F)[seq(1,nrow(BreastCancer[ind == 2,])*2,2)] # model using ctree with weights 10:1 (benign:malignant) x.ctw2 <- ctree(Class ~ ., data=BreastCancer[ind == 1,], weights= ifelse(BreastCancer[ind == 1,]$Class=='benign', 10, 1)) x.ctw2.pred <- predict(x.ctw2, newdata=BreastCancer[ind == 2,]) x.ctw2.prob <- 1- unlist(treeresponse(x.ctw2, BreastCancer[ind == 2,]), use.names=F)[seq(1,nrow(BreastCancer[ind == 2,])*2,2)] # Output the plot to a PNG file for display on web. To draw to the screen, # comment this line out. png(filename="roc_curve_weights.png", width=700, height=700) # plot performance require(ROCR) # create an ROCR prediction object from probabilities x.ct.prob.rocr <- prediction(x.ct.prob, BreastCancer[ind == 2,'Class']) # prepare an ROCR performance object for ROC curve (tpr=true positive rate, fpr=false positive rate) x.ct.perf <- performance(x.ct.prob.rocr, "tpr","fpr") # plot it plot(x.ct.perf, col=2, main="ROC curves comparing classification performance of ctree weighted vs unweighted") # Draw a legend. legend(0.6, 0.6, c('unweighted', 'weighted 1:10 (benign:malignant)', 'weighted 10:1'), 2:4) # weighted 1:10 x.ctw.prob.rocr <- prediction(x.ctw.prob, BreastCancer[ind == 2,'Class']) x.ctw.perf <- performance(x.ctw.prob.rocr, "tpr","fpr") # add=TRUE draws on the existing chart plot(x.ctw.perf, col=3, add=TRUE) # weighted 10:1 x.ctw2.prob.rocr <- prediction(x.ctw2.prob, BreastCancer[ind == 2,'Class']) x.ctw2.perf <- performance(x.ctw2.prob.rocr, "tpr","fpr") # add=TRUE draws on the existing chart plot(x.ctw2.perf, col=4, add=TRUE) # close and save PNG dev.off()

This ROC curve compares the performance of the decision tree model unweighted, weighted 1:10, and weighted 10:1.

To use weights with cforest (random forests), simply search and replace ctree with cforest: all the other parameters and code are identical.

Thanks for posting this! Three years on, this is exactly what I am looking for, for my current analyses.