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.
Version: 0.0.5 Depends: partykit, R (≥ 3.5.0) Imports: doParallel, foreach, glue, graphics, grid, lifecycle, magrittr, nnet, parallel, Pursuit, Rcpp, rlang (≥ 0.4.11), stats, rpart, methods, glmnet LinkingTo: Rcpp, RcppArmadillo, RcppEigen Suggests: knitr, rmarkdown, spelling, testthat (≥ 3.0.0) Published: 2025-04-25 DOI: 10.32614/CRAN.package.ODRF Author: Yu Liu [aut, cre, cph], Yingcun Xia [aut] Maintainer: Yu Liu <liuyuchina123 at gmail.com> BugReports: https://github.com/liuyu-star/ODRF/issues License: GPL (≥ 3) URL: https://liuyu-star.github.io/ODRF/ NeedsCompilation: yes Language: en-US Citation: ODRF citation info Materials: README NEWS CRAN checks: ODRF resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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