Functional gradient descent algorithm for a variety of convex and nonconvex loss functions, for both classical and robust regression and classification problems. HingeBoost is implemented for binary and multi-class classification, with unequal misclassification costs for binary case. The algorithm can fit linear and nonlinear classifiers.
| Version: | 0.3-12 |
| Depends: | gbm |
| Imports: | rpart, methods, foreach, doParallel |
| Suggests: | hdi, pROC, R.rsp, knitr |
| Published: | 2016-01-04 |
| Author: | Zhu Wang [aut, cre], Torsten Hothorn [ctb] |
| Maintainer: | Zhu Wang <zwang at connecticutchildrens.org> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| In views: | MachineLearning |
| CRAN checks: | bst results |
| Reference manual: | bst.pdf |
| Vignettes: |
Classification of Cancer Types Using Gene Expression Data (Long) Classification of UCI Machine Learning Datasets (Long) Classification of UCI Machine Learning Datasets (Short) Classification of Cancer Types Using Gene Expression Data (Short) Cancer Classification Using Mass Spectrometry-based Proteomics Data |
| Package source: | bst_0.3-12.tar.gz |
| Windows binaries: | r-devel: bst_0.3-12.zip, r-release: bst_0.3-12.zip, r-oldrel: bst_0.3-12.zip |
| OS X Snow Leopard binaries: | r-release: bst_0.3-12.tgz, r-oldrel: bst_0.3-4.tgz |
| OS X Mavericks binaries: | r-release: bst_0.3-12.tgz |
| Old sources: | bst archive |
| Reverse imports: | bujar |
| Reverse suggests: | fscaret, mlr |