Compare performance of machine learning classifiers in R

This tutorial demonstrates to the R novice how to create five machine learning models for classification and compare the performance graphically with ROC curves in one chart.

For a simpler introduction, start with Plot ROC curve and lift chart in R.

# 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)
# some algorithms don't like missing values, so remove rows with missing values
BreastCancer <- na.omit(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))

# create model using recursive partitioning on the training data set
require(rpart)
x.rp <- rpart(Class ~ ., data=BreastCancer[ind == 1,])
# predict classes for the evaluation data set
x.rp.pred <- predict(x.rp, type="class", newdata=BreastCancer[ind == 2,])
# score the evaluation data set (extract the probabilities)
x.rp.prob <- predict(x.rp, type="prob", newdata=BreastCancer[ind == 2,])

# To view the decision tree, uncomment this line.
# plot(x.rp, main="Decision tree created using rpart")

# create model using conditional inference trees
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)]

# To view the decision tree, uncomment this line.
# plot(x.ct, main="Decision tree created using condition inference trees")

# create model using random forest and bagging ensemble using conditional inference trees
x.cf <- cforest(Class ~ ., data=BreastCancer[ind == 1,], control = cforest_unbiased(mtry = ncol(BreastCancer)-2))
x.cf.pred <- predict(x.cf, newdata=BreastCancer[ind == 2,])
x.cf.prob <-  1- unlist(treeresponse(x.cf, BreastCancer[ind == 2,]), use.names=F)[seq(1,nrow(BreastCancer[ind == 2,])*2,2)]

# create model using bagging (bootstrap aggregating)
require(ipred)
x.ip <- bagging(Class ~ ., data=BreastCancer[ind == 1,])
x.ip.prob <- predict(x.ip, type="prob", newdata=BreastCancer[ind == 2,])

# create model using svm (support vector machine)
require(e1071)
# svm requires tuning
x.svm.tune <- tune(svm, Class~., data = BreastCancer[ind == 1,],
                   ranges = list(gamma = 2^(-8:1), cost = 2^(0:4)),
                   tunecontrol = tune.control(sampling = "fix"))
# display the tuning results (in text format)
x.svm.tune
# If the tuning results are on the margin of the parameters (e.g., gamma = 2^-8), 
# then widen the parameters.
# I manually copied the cost and gamma from console messages above to parameters below.
x.svm <- svm(Class~., data = BreastCancer[ind == 1,], cost=4, gamma=0.0625, probability = TRUE)
x.svm.prob <- predict(x.svm, type="prob", newdata=BreastCancer[ind == 2,], probability = TRUE)


##
## plot ROC curves to compare the performance of the individual classifiers
##

# Output the plot to a PNG file for display on web.  To draw to the screen, 
# comment this line out.
png(filename="roc_curve_5_models.png", width=700, height=700)

# load the ROCR package which draws the ROC curves
require(ROCR)

# create an ROCR prediction object from rpart() probabilities
x.rp.prob.rocr <- prediction(x.rp.prob[,2], BreastCancer[ind == 2,'Class'])
# prepare an ROCR performance object for ROC curve (tpr=true positive rate, fpr=false positive rate)
x.rp.perf <- performance(x.rp.prob.rocr, "tpr","fpr")
# plot it
plot(x.rp.perf, col=2, main="ROC curves comparing classification performance of five machine learning models")

# Draw a legend.
legend(0.6, 0.6, c('rpart', 'ctree', 'cforest','bagging','svm'), 2:6)

# ctree
x.ct.prob.rocr <- prediction(x.ct.prob, BreastCancer[ind == 2,'Class'])
x.ct.perf <- performance(x.ct.prob.rocr, "tpr","fpr")
# add=TRUE draws on the existing chart 
plot(x.ct.perf, col=3, add=TRUE)

# cforest
x.cf.prob.rocr <- prediction(x.cf.prob, BreastCancer[ind == 2,'Class'])
x.cf.perf <- performance(x.cf.prob.rocr, "tpr","fpr")
plot(x.cf.perf, col=4, add=TRUE)

# bagging
x.ip.prob.rocr <- prediction(x.ip.prob[,2], BreastCancer[ind == 2,'Class'])
x.ip.perf <- performance(x.ip.prob.rocr, "tpr","fpr")
plot(x.ip.perf, col=5, add=TRUE)

# svm
x.svm.prob.rocr <- prediction(attr(x.svm.prob, "probabilities")[,2], BreastCancer[ind == 2,'Class'])
x.svm.perf <- performance(x.svm.prob.rocr, "tpr","fpr")
plot(x.svm.perf, col=6, add=TRUE)


# Close and save the PNG file.
dev.off()

Here are some exercises left for the reader:

• Is the performance good for a medical diagnostic? How about for a direct mailing campaign?
• Can rpart() performance be improved? Is rpart() overfitting? Tweak its parameters.
• For printing on paper, output to PDF or SVG instead of PNG.
• Add another classifier algorithm or tweak the settings of an existing classifier (but plot it as a separate ROC curve). Hint: the randomForest() function may get confused because the covariates are factors.
• Create a generic R function to abstract the process of adding another classifier.
• Switch from the BreastCancer to the kyphosis data set.
• Adapt the generic function above so it accepts arbitrary data sets.

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