Advanced Analytical Theory and Methods: Classification - Data Science and Big Data Analytics - page 292

or no referring to the Play action of whether to play outside. Note that, by default,
R has converted the values of Wind ( True/False ) into numbers.
rpart.plot(fit, type=4, extra=1)
Figure 7.9 A decision tree built from DTdata.csv
The decisions in Figure 7.9 are abbreviated. Use the following command to spell
out the full names and display the classification rate at each node.
rpart.plot(fit, type=4, extra=2, clip.right.labs=FALSE,
varlen=0, faclen=0)
The decision tree can be used to predict outcomes for new datasets. Consider a
testing set that contains the following record.
Outlook="rainy", Temperature="mild", Humidity="high",
Wind=FALSE
The goal is to predict the play decision of this record. The following code loads the
data into R as a data frame newdata . Note that the training set does not contain
this case.
newdata <- data.frame(Outlook="rainy", Temperature="mild",
Humidity="high", Wind=FALSE)
newdata
Outlook Temperature Humidity Wind
1 rainy mild high FALSE