Metadata-Version: 2.1
Name: ranktreeEnsemble
Version: 0.1.3
Summary: 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.
Home-page: https://github.com/RuijieYin/Ensemble_Methods_of_Rank_Based_Trees_py
Author: ['Ruijie Yin', 'Ye Chen', 'Min Lu']
Author-email: ruijieyin428@gmail.com
Maintainer: Ruijie Yin
Maintainer-email: ruijieyin428@gmail.com
License: MIT License
Keywords: ensemble,rank trees
Platform: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas
Requires-Dist: sklearn
Requires-Dist: numpy (==1.18.1)
Requires-Dist: shap-hypetune
Requires-Dist: lightgbm

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.

