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= 158807 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 158807 34370 No (0.7835738 0.2164262)  
##    2) humidity_3pm< 71.5 132550 18502 No (0.8604149 0.1395851) *
##    3) humidity_3pm>=71.5 26257 10389 Yes (0.3956659 0.6043341)  
##      6) humidity_3pm< 83.5 15155  7199 No (0.5249753 0.4750247)  
##       12) wind_gust_speed< 42 9116  3442 No (0.6224221 0.3775779) *
##       13) wind_gust_speed>=42 6039  2282 Yes (0.3778771 0.6221229) *
##      7) humidity_3pm>=83.5 11102  2433 Yes (0.2191497 0.7808503) *

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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