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ranktreeEnsemble (version 0.23)

Ensemble Models of Rank-Based Trees with Extracted Decision Rules

Description

Fast computing an ensemble of rank-based trees via boosting or random forest on binary and multi-class problems. It converts continuous gene expression profiles into ranked gene pairs, for which the variable importance indices are computed and adopted for dimension reduction. Decision rules can be extracted from trees.

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Install

install.packages('ranktreeEnsemble')

Monthly Downloads

517

Version

0.23

License

GPL (>= 2)

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Repository

https://github.com/TransBioInfoLab/ranktreeEnsemble/

Maintainer

Min Lu

Last Published

May 24th, 2024

Functions in ranktreeEnsemble (0.23)

Gene expression profiles in triple-negative breast cancer cell

Random Forest via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Extract Interpretable Decision Rules from a Random Forest Model

Select Decision Rules to Achieve Higher Prediction Accuracy

ranktreeEnsemble

Ensemble Models of Rank-Based Trees for Single Sample Classification with Interpretable Rules

Transform Continuous Variables into Ranked Binary Pairs

Generalized Boosted Modeling via Rank-Based Trees for Single Sample Classification with Gene Expression Profiles

Prediction or Extract Predicted Values for Random Forest, Random Forest Rule or Boosting Models

Variable Importance Index for Each Predictor