14.1 ML Setup

20210102 Packages used in this chapter include ROCR (Sing et al. 2020), ggplot2 (Wickham et al. 2024), rpart (Therneau and Atkinson 2023), and rattle (G. Williams 2024).

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(ROCR)         # Use prediction() for evaluation.
library(ggplot2)      # Display evaluations.
library(rattle)       # Dataset: weather.
library(rpart)        # ML: decision tree rpart().
library(scales)       # Support: commas(), percent().

References

Sing, Tobias, Oliver Sander, Niko Beerenwinkel, and Thomas Lengauer. 2020. ROCR: Visualizing the Performance of Scoring Classifiers. http://ipa-tys.github.io/ROCR/.

Therneau, Terry, and Beth Atkinson. 2023. Rpart: Recursive Partitioning and Regression Trees. https://github.com/bethatkinson/rpart.

Wickham, Hadley, Winston Chang, Lionel Henry, Thomas Lin Pedersen, Kohske Takahashi, Claus Wilke, Kara Woo, Hiroaki Yutani, Dewey Dunnington, and Teun van den Brand. 2024. Ggplot2: Create Elegant Data Visualisations Using the Grammar of Graphics. https://ggplot2.tidyverse.org.

Williams, Graham. 2024. Rattle: Graphical User Interface for Data Science in r. https://rattle.togaware.com/.

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