Package: inTrees 1.5
inTrees: Interpret Tree Ensembles
For tree ensembles such as random forests, regularized random forests and gradient boosted trees, this package provides functions for: extracting, measuring and pruning rules; selecting a compact rule set; summarizing rules into a learner; calculating frequent variable interactions; formatting rules in latex code. Reference: Interpreting tree ensembles with inTrees (Houtao Deng, 2019, <doi:10.1007/s41060-018-0144-8>).
Authors:
inTrees_1.5.tar.gz
inTrees_1.5.zip(r-4.5)inTrees_1.5.zip(r-4.4)inTrees_1.5.zip(r-4.3)
inTrees_1.5.tgz(r-4.4-any)inTrees_1.5.tgz(r-4.3-any)
inTrees_1.5.tar.gz(r-4.5-noble)inTrees_1.5.tar.gz(r-4.4-noble)
inTrees_1.5.tgz(r-4.4-emscripten)inTrees_1.5.tgz(r-4.3-emscripten)
inTrees.pdf |inTrees.html✨
inTrees/json (API)
NEWS
| # Install 'inTrees' in R: |
| install.packages('inTrees', repos = c('https://softwaredeng.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/softwaredeng/intrees/issues
Last updated 5 months agofrom:f30a876761. Checks:OK: 7. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Aug 21 2024 |
| R-4.5-win | OK | Aug 21 2024 |
| R-4.5-linux | OK | Aug 21 2024 |
| R-4.4-win | OK | Aug 21 2024 |
| R-4.4-mac | OK | Aug 21 2024 |
| R-4.3-win | OK | Aug 21 2024 |
| R-4.3-mac | OK | Aug 21 2024 |
Exports:applyLearnerbuildLearnercomputeRuleInfordataSimulatedicretizeVectorextractRulesformatGBMGBM2ListgetFreqPatterngetRuleMetricgetTypeXlookupRulemeasureRuleNum2LevelpresentRulespruneRulepruneSingleRuleRF2Listrule2TableruleList2ExecselectRuleRRFsingleRuleList2ExecsortRuletreeVisitvoteAllRulesXGB2List
Dependencies:arulesdata.tablegbmgenericsjsonlitelatticeMatrixRRFsurvivalxgboostxtable
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| apply a simplified tree ensemble learner (STEL) to data | applyLearner |
| build a simplified tree ensemble learner (STEL) | buildLearner |
| Simulate data | dataSimulate |
| discretize a variable | dicretizeVector |
| Extract rules from a list of trees | extractRules |
| Transform gbm object to a list of trees | GBM2List |
| calculate frequent variable interactions | getFreqPattern |
| Assign outcomes to a conditions, and measure the rules | getRuleMetric |
| Present a learner using column names instead of X[i,] | presentRules |
| Prune irrevant variable-value pair from a rule condition | pruneRule |
| Transform a random forest object to a list of trees | RF2List |
| select a set of relevant and non-redundant rules | selectRuleRRF |
| Transform an xgboost object to a list of trees | XGB2List |
