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: |
0.3-11 |
| Depends: |
gbm |
| Imports: |
rpart, methods, foreach, doParallel |
| Suggests: |
hdi, pROC |
| Published: |
2015-12-19 |
| 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 |