20.1 Decision Trees Setup

20180603 Packages used in this chapter include C50 (Kuhn and Quinlan 2025), RWeka (Hornik 2026), party (Hothorn et al. 2026), partykit (Hothorn and Zeileis 2026), rpart (Therneau and Atkinson 2026), rpart.plot (Milborrow 2026), and rattle (G. Williams 2026).

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 variable ds and further template variables are setup as introduced by Graham J. Williams (2017). See Chapter 8 for details.

dsname <- "weatherAUS"
ds     <- get(dsname)
    
nobs   <- nrow(ds)

vnames <- names(ds)
ds    %<>% clean_names(numerals="right")
names(vnames) <- names(ds)

vars   <- names(ds)
target <- "rain_tomorrow"
vars   <- c(target, vars) %>% unique() %>% rev()

It is always useful to remind ourselves of the dataset with a random sample:

ds  %>% sample_frac() %>% select(date, location, sample(3:length(vars), 5))
## # A tibble: 275,410 × 7
##    date       location    pressure_3pm sunshine temp_9am max_temp wind_speed_9am
##    <date>     <chr>              <dbl>    <dbl>    <dbl>    <dbl>          <dbl>
##  1 2015-04-06 Bendigo            1005.     NA       12.6     18.1              0
##  2 2010-05-25 Launceston         1014.     NA        9.5     15.3              7
##  3 2024-09-17 Launceston           NA      NA       11.1     15.4             20
##  4 2013-11-12 NorahHead          1012.     NA       17.5     21                9
....

References

Hornik, Kurt. 2026. RWeka: R/Weka Interface. https://doi.org/10.32614/CRAN.package.RWeka.
Hothorn, Torsten, Kurt Hornik, Carolin Strobl, and Achim Zeileis. 2026. Party: A Laboratory for Recursive Partytioning. https://codeberg.org/thothorn/party.
Hothorn, Torsten, and Achim Zeileis. 2026. Partykit: A Toolkit for Recursive Partytioning. https://codeberg.org/thothorn/partykit.
Kuhn, Max, and Ross Quinlan. 2025. C50: C5.0 Decision Trees and Rule-Based Models. https://topepo.github.io/C5.0/.
Milborrow, Stephen. 2026. Rpart.plot: Plot Rpart Models: An Enhanced Version of Plot.rpart. http://www.milbo.org/rpart-plot/index.html.
Therneau, Terry, and Beth Atkinson. 2026. Rpart: Recursive Partitioning and Regression Trees. https://github.com/bethatkinson/rpart.
Williams, Graham. 2026. Rattle: R Data Science Supporting Rattle. https://togaware.com/projects/rattle/.
Williams, Graham J. 2017. The Essentials of Data Science: Knowledge Discovery Using r. The r Series. CRC Press.

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