JOUSBoost: JOUSBoost: A package for probability estimation
In JOUSBoost: Implements Under/Oversampling for Probability Estimation
Description
Details
References
Description
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.
Details
For more theoretical background, consult Mease (2007).
References
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 May 2, 2019, 6:03 a.m.
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Description Details References
Description
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.
Details
For more theoretical background, consult Mease (2007).
References
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.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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