18.6 Decision Trees

20210103

Representation	Method	Measure
Tree	Recursive Partitioning	Information Gain

To build a decision tree we typically use rpart::rpart().

mtype <- "rpart"
mdesc <- "decision tree"

ds %>%
  select(all_of(vars)) %>%
  slice(tr) %>%
  rpart(form, ., method="class", control=rpart.control(maxdepth=3)) %T>%
  print() ->
model

## n= 123722 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 123722 25943 No (0.7903122 0.2096878)  
##    2) humidity_3pm< 72.5 105663 14881 No (0.8591655 0.1408345) *
##    3) humidity_3pm>=72.5 18059  6997 Yes (0.3874522 0.6125478)  
##      6) humidity_3pm< 83.5 10136  4855 No (0.5210142 0.4789858)  
##       12) rainfall< 2.7 6884  2721 No (0.6047356 0.3952644) *
##       13) rainfall>=2.7 3252  1118 Yes (0.3437884 0.6562116) *
##      7) humidity_3pm>=83.5 7923  1716 Yes (0.2165846 0.7834154) *

Chapter 20 covers decision trees in detail whilst Chapter 14 uses decision trees as the model builder to demonstrate the model template. Examples of decision tree induction are available through the rain, iris, and pyiris packages from MLHub.

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