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= 192787 
## 
## node), split, n, loss, yval, (yprob)
##       * denotes terminal node
## 
##  1) root 192787 41266 No (0.7859503 0.2140497)  
##    2) humidity_3pm< 72.5 163203 23032 No (0.8588751 0.1411249) *
##    3) humidity_3pm>=72.5 29584 11350 Yes (0.3836533 0.6163467)  
##      6) humidity_3pm< 82.5 15378  7234 No (0.5295877 0.4704123)  
##       12) wind_gust_speed< 42 9292  3508 No (0.6224709 0.3775291) *
##       13) wind_gust_speed>=42 6086  2360 Yes (0.3877752 0.6122248) *
##      7) humidity_3pm>=82.5 14206  3206 Yes (0.2256793 0.7743207) *

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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