Package: sbrl 1.4
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.zip(r-4.7)sbrl_1.4.zip(r-4.6)sbrl_1.4.zip(r-4.5)
sbrl_1.4.tgz(r-4.6-x86_64)sbrl_1.4.tgz(r-4.6-arm64)sbrl_1.4.tgz(r-4.5-x86_64)sbrl_1.4.tgz(r-4.5-arm64)
sbrl_1.4.tar.gz(r-4.7-arm64)sbrl_1.4.tar.gz(r-4.7-x86_64)sbrl_1.4.tar.gz(r-4.6-arm64)sbrl_1.4.tar.gz(r-4.6-x86_64)
sbrl_1.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
sbrl/json (API)
| # Install 'sbrl' in R: |
| install.packages('sbrl', repos = c('https://hongyuy.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 from:76a0e9f5ee. Checks:13 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 130 | ||
| linux-devel-x86_64 | OK | 124 | ||
| source / vignettes | OK | 166 | ||
| linux-release-arm64 | OK | 136 | ||
| linux-release-x86_64 | OK | 122 | ||
| macos-release-arm64 | OK | 182 | ||
| macos-release-x86_64 | OK | 230 | ||
| macos-oldrel-arm64 | OK | 99 | ||
| macos-oldrel-x86_64 | OK | 344 | ||
| windows-devel | OK | 119 | ||
| windows-release | OK | 116 | ||
| windows-oldrel | OK | 105 | ||
| wasm-release | OK | 122 |
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 |
