20.1 Decision Trees Setup
20180603 Packages used in this chapter include C50 (Kuhn and Quinlan 2023), RWeka (Hornik 2023), party (Hothorn et al. 2022), partykit (Hothorn and Zeileis 2022), rpart (Therneau and Atkinson 2022), rpart.plot (Milborrow 2022), and rattle (G. Williams 2022).
Packages are loaded into the currently running R session from your
local library directories on disk. Missing packages can be installed
using utils::install.packages() within R. On Ubuntu, for
example, R packages can also be installed using
$ wajig install r-cran-<pkgname>.
# Load required packages from local library into the R session. library(C50) # Original C5.0 implementation. library(RWeka) # Weka decision tree J48. library(party) # Conditional decision trees ctree(). library(partykit) # Convert rpart object to BinaryTree library(rattle) # GUI for building trees and fancy tree plot. library(rpart) # Popular decision tree algorithm. library(rpart.plot) # Enhanced tree plots.
The rattle::weatherAUS dataset is loaded into the template
ds and further template variables are setup as
introduced by Graham J. Williams (2017). See
Chapter 8 for details.
<- "weatherAUS" dsname <- get(dsname) ds <- nrow(ds) nobs <- names(ds) vnames %<>% clean_names(numerals="right") ds names(vnames) <- names(ds) <- names(ds) vars <- "rain_tomorrow" target <- c(target, vars) %>% unique() %>% rev()vars
It is always useful to remind ourselves of the dataset with a random sample:
%>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))ds
## # A tibble: 217,049 × 7 ## date location wind_speed_3pm temp_9am rain_today cloud_3pm rainf…¹ ## <date> <chr> <dbl> <dbl> <fct> <int> <dbl> ## 1 2014-04-06 AliceSprings 6 24 No 7 0 ## 2 2009-06-23 Perth 15 10.8 Yes 8 1.6 ## 3 2013-08-06 MountGambier 28 10.9 Yes 3 10.4 ## 4 2012-04-20 WaggaWagga 7 17.3 No 6 0.6 ....
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