Togaware DATA MINING
Desktop Survival Guide
by Graham Williams
Google

Tuning rpart

To keep the examples simple we use the audit dataset and remove entities with missing values and also ignore the Adjustment column.

library(e1071)
audit <- na.omit(read.csv("audit.csv"))
audit$Adjustment <- NULL
fm <- formula(Adjusted ~ ID+Age+Employment+Education+
                         Marital+Occupation+Income+Sex+
                         Deductions+Hours+Accounts)
# Explore minsplit
audit.rpart <- tune.rpart(fm, data=audit, minsplit=seq(10,100,10))
plot(audit.rpart, main="Tune rpart on minsplit")




# cp
audit.rpart <- tune.rpart(fm, data = audit, cp = c(0.002,0.005,0.01,0.015,0.02,0.03))
plot(audit.rpart,main="Performance of rpart vs. cp")
readline()

# maxdepth
audit.rpart <- tune.rpart(fm, data = audit, maxdepth = 1:5)
plot(audit.rpart,main="Performance of rpart vs. cp")
readline()



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