mboost v0.5-4
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Model-Based Boosting
Functional gradient descent algorithms
(boosting) for optimizing general loss functions utilizing
componentwise least squares, either of parametric linear form or
smoothing splines, or regression trees as base learners for fitting
generalized linear, additive and interaction models to potentially
high-dimensional data.
Functions in mboost
| Name | Description | |
| Family | Gradient Boosting Families | |
| FP | Fractional Polynomials | |
| bodyfat | Prediction of Body Fat by Skinfold Thickness, Circumferences, and Bone Breadths | |
| glmboost | Gradient Boosting with Component-wise Linear Models | |
| blackboost | Gradient Boosting with Regression Trees | |
| methods | Methods for Gradient Boosting Objects | |
| boost_control | Control Hyper Parameters for Boosting Algorithms | |
| cvrisk | Cross-Validation | |
| boost_dpp | Data Preprocessing for Gradient Boosting | |
| westbc | Breast Cancer Gene Expression | |
| wpbc | Wisconsin Prognostic Breast Cancer Data | |
| IPCweights | Inverse Probability of Censoring Weights | |
| boost_family-class | Class "boost_family": Gradient Boosting Family | |
| gamboost | Gradient Boosting with Component-wise Smoothing Splines | |
| No Results! | ||
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Details
| Date | $Date: 2007/04/18 08:28:35 $ |
| SaveImage | yes |
| License | GPL |
| Packaged | Thu Apr 19 17:55:13 2007; hothorn |
| depends | base (>= 2.3.0) , methods , modeltools (>= 0.2.10) , party (>= 0.9-10) , R (>= 2.3.0) , splines |
| suggests | ipred , mlbench |
| Contributors | Peter Buhlmann, Torsten Hothorn |
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