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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