Package: broomstick 0.1.2.9200
broomstick: Convert Decision Tree Objects into Tidy Data Frames
Convert Decision Tree objects into tidy data frames, by using the framework laid out by the package broom, this means that decision tree output can be easily reshaped, porocessed, and combined with tools like 'dplyr', 'tidyr' and 'ggplot2'. Like the package broom, broomstick provides three S3 generics: tidy, to summarise decision tree specific features - tidy returns the variable importance table; augment adds columns to the original data such as predictions and residuals; and glance, which provides a one-row summary of model-level statistics.
Authors:
broomstick_0.1.2.9200.tar.gz
broomstick_0.1.2.9200.zip(r-4.5)broomstick_0.1.2.9200.zip(r-4.4)broomstick_0.1.2.9200.zip(r-4.3)
broomstick_0.1.2.9200.tgz(r-4.4-any)broomstick_0.1.2.9200.tgz(r-4.3-any)
broomstick_0.1.2.9200.tar.gz(r-4.5-noble)broomstick_0.1.2.9200.tar.gz(r-4.4-noble)
broomstick_0.1.2.9200.tgz(r-4.4-emscripten)broomstick_0.1.2.9200.tgz(r-4.3-emscripten)
broomstick.pdf |broomstick.html✨
broomstick/json (API)
NEWS
# Install 'broomstick' in R: |
install.packages('broomstick', repos = c('https://njtierney.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/njtierney/broomstick/issues
broomdecision-treesgbmmachine-learningrandomforestrpartstatistical-learning
Last updated 12 months agofrom:5215feed37. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 04 2024 |
R-4.5-win | NOTE | Nov 04 2024 |
R-4.5-linux | NOTE | Nov 04 2024 |
R-4.4-win | NOTE | Nov 04 2024 |
R-4.4-mac | NOTE | Nov 04 2024 |
R-4.3-win | NOTE | Nov 04 2024 |
R-4.3-mac | NOTE | Nov 04 2024 |
Dependencies:backportsbroomclicpp11dplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigpurrrR6rlangstringistringrtibbletidyrtidyselectutf8vctrswithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Tidying methods for a randomForest model | augment.randomForest glance.randomForest rf_tidiers tidy.randomForest |
Augment your model object | augment.rpart |
Convert Decision Tree Analysis Objects into Tidy Data Frames | broomstick-package broomstick |
tidy up the model summary of gbm | tidy.gbm |
tidy up the model summary of rpart | tidy.rpart |