JOUSBoost: JOUSBoost: A package for probability estimation

Description Details References


JOUSBoost implements under/oversampling with jittering for probability estimation. Its intent is to be used to improve probability estimates that come from boosting algorithms (such as AdaBoost), but is modular enough to be used with virtually any classification algorithm from machine learning.


For more theoretical background, consult Mease (2007).


Mease, D., Wyner, A. and Buja, A. (2007). Costweighted boosting with jittering and over/under-sampling: JOUS-boost. J. Machine Learning Research 8 409-439.

JOUSBoost documentation built on July 13, 2017, 1:03 a.m.