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ODRF

Oblique Decision Random Forest for Classification and Regression

v0.0.5 · Apr 25, 2025 · GPL (>= 3)

Description

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.

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NOTE r-oldrel-macos-arm64

installed package size

  installed size is 14.1Mb
  sub-directories of 1Mb or more:
    libs  13.4Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is 14.0Mb
  sub-directories of 1Mb or more:
    libs  13.2Mb
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NOTE 12 OK · 2 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026
NOTE r-oldrel-macos-arm64

installed package size

  installed size is 14.1Mb
  sub-directories of 1Mb or more:
    libs  13.4Mb
NOTE r-oldrel-macos-x86_64

installed package size

  installed size is 14.0Mb
  sub-directories of 1Mb or more:
    libs  13.2Mb

Dependency Network

Dependencies Reverse dependencies partykit doParallel foreach glue lifecycle magrittr nnet Pursuit Rcpp rlang rpart glmnet ODRF

Version History

new 0.0.5 Mar 9, 2026