Package: ODRF 0.0.4.9002
ODRF: Oblique Decision Random Forest for Classification and Regression
The oblique decision tree (ODT) uses linear combinations of predictors as partitioning variables in a decision tree. Oblique Decision Random Forest (ODRF) is an ensemble of multiple ODTs generated by feature bagging. Oblique Decision Boosting Tree (ODBT) applies feature bagging during the training process of ODT-based boosting trees to ensemble multiple boosting trees. All three methods can be used for classification and regression, and ODT and ODRF serve as supplements to the classical CART of Breiman (1984) <doi:10.1201/9781315139470> and Random Forest of Breiman (2001) <doi:10.1023/A:1010933404324> respectively.
Authors:
ODRF_0.0.4.9002.tar.gz
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ODRF.pdf |ODRF.html✨
ODRF/json (API)
NEWS
# Install 'ODRF' in R: |
install.packages('ODRF', repos = c('https://liuyu-star.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/liuyu-star/odrf/issues
Last updated 28 days agofrom:863dfa0b0b. Checks:OK: 1 NOTE: 8. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 09 2024 |
R-4.5-win-x86_64 | NOTE | Oct 09 2024 |
R-4.5-linux-x86_64 | NOTE | Oct 09 2024 |
R-4.4-win-x86_64 | NOTE | Oct 09 2024 |
R-4.4-mac-x86_64 | NOTE | Oct 09 2024 |
R-4.4-mac-aarch64 | NOTE | Oct 09 2024 |
R-4.3-win-x86_64 | NOTE | Oct 09 2024 |
R-4.3-mac-x86_64 | NOTE | Oct 09 2024 |
R-4.3-mac-aarch64 | NOTE | Oct 09 2024 |
Exports:%>%Accuracybest.cut.nodedefaultsODBTODRFODTonlineplot_ODT_depthPPOpruneRandRotRotMatMakeRotMatPPORotMatRandRotMatRFVarImp
Dependencies:clicodetoolsdoParallelforeachFormulaglmnetglueinumiteratorslatticelibcoinlifecyclemagrittrMASSMatrixmvtnormnnetpartykitPursuitRcppRcppArmadilloRcppEigenrlangrpartshapesurvival