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= 145946 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 145946 31010 No (0.7875242 0.2124758)  
##    2) humidity_3pm< 71.5 122254 16843 No (0.8622295 0.1377705) *
##    3) humidity_3pm>=71.5 23692  9525 Yes (0.4020344 0.5979656)  
##      6) humidity_3pm< 82.5 12923  5906 No (0.5429854 0.4570146)  
##       12) rainfall< 2.05 8400  3098 No (0.6311905 0.3688095) *
##       13) rainfall>=2.05 4523  1715 Yes (0.3791731 0.6208269) *
##      7) humidity_3pm>=82.5 10769  2508 Yes (0.2328907 0.7671093) *

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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