Package: sbrl 1.4
Hongyu Yang
sbrl: Scalable Bayesian Rule Lists Model
An efficient implementation of Scalable Bayesian Rule Lists Algorithm, a competitor algorithm for decision tree algorithms; see Hongyu Yang, Cynthia Rudin, Margo Seltzer (2017) <https://proceedings.mlr.press/v70/yang17h.html>. It builds from pre-mined association rules and have a logical structure identical to a decision list or one-sided decision tree. Fully optimized over rule lists, this algorithm strikes practical balance between accuracy, interpretability, and computational speed.
Authors:
sbrl_1.4.tar.gz
sbrl_1.4.tar.gz(r-4.5-noble)sbrl_1.4.tar.gz(r-4.4-noble)
sbrl_1.4.tgz(r-4.4-emscripten)sbrl_1.4.tgz(r-4.3-emscripten)
sbrl.pdf |sbrl.html✨
sbrl/json (API)
| # Install 'sbrl' in R: |
| install.packages('sbrl', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- tictactoe - SHUFFLED TIC-TAC-TOE-ENDGAME DATASET
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 months agofrom:76a0e9f5ee. Checks:OK: 2. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 25 2024 |
| R-4.5-linux-x86_64 | OK | Dec 25 2024 |
Exports:get_data_feature_matpredict.sbrlprint.sbrlsbrlshow.sbrl
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| SCALABLE BAYESIAN RULE LISTS | sbrl-package |
| GET BINARY MATRIX REPRESENTATION OF THE DATA-FEATURE RELAITONSHIP | get_data_feature_mat |
| PREDICT THE POSITIVE PROBABILITY FOR THE OBSERVATIONS | predict predict.sbrl |
| INTERPRETABLE VERSION OF A SBRL MODEL | print.sbrl show.sbrl |
| fit the scalable bayesian rule lists model | sbrl |
| SHUFFLED TIC-TAC-TOE-ENDGAME DATASET | tictactoe |
