# gbm v2.1.5

Monthly downloads

## Generalized Boosted Regression Models

An implementation of extensions to Freund and Schapire's AdaBoost
algorithm and Friedman's gradient boosting machine. Includes regression
methods for least squares, absolute loss, t-distribution loss, quantile
regression, logistic, multinomial logistic, Poisson, Cox proportional hazards
partial likelihood, AdaBoost exponential loss, Huberized hinge loss, and
Learning to Rank measures (LambdaMart). Originally developed by Greg Ridgeway.

## Readme

# gbm

## Overview

The gbm package (which stands for **g**eneralized **b**oosted
**m**odels) implements extensions to Freund and Schapire’s AdaBoost
algorithm and Friedman’s gradient boosting
machine. It includes
regression methods for least squares, absolute loss, t-distribution
loss, quantile regression, logistic, multinomial logistic, Poisson, Cox
proportional hazards partial likelihood, AdaBoost exponential loss,
Huberized hinge loss, and Learning to Rank measures (i.e.,
LambdaMart).

## Installation

```
# The easiest way to get gbm is to it install from CRAN:
install.packages("gbm")
# Or the the development version from GitHub:
# install.packages("devtools")
devtools::install_github("gbm-developers/gbm")
```

## Lifecycle

The gbm package is retired and no longer under active development. We will only make the necessary changes to ensure that gbm remain on CRAN. For the most part, no new features will be added, and only the most critical of bugs will be fixed.

This is a maintained version of `gbm`

back compatible to CRAN versions
of `gbm`

2.1.x. It exists mainly for the purpose of reproducible
research and data analyses performed with the 2.1.x versions of `gbm`

.
For newer development, and a more consistent API, try out the
gbm3 package!

## Functions in gbm

Name | Description | |

gbm.roc.area | Compute Information Retrieval measures. | |

gbmCrossVal | Cross-validate a gbm | |

plot.gbm | Marginal plots of fitted gbm objects | |

interact.gbm | Estimate the strength of interaction effects | |

predict.gbm | Predict method for GBM Model Fits | |

pretty.gbm.tree | Print gbm tree components | |

relative.influence | Methods for estimating relative influence | |

reconstructGBMdata | Reconstruct a GBM's Source Data | |

grid.arrange | Arrange multiple grobs on a page | |

print.gbm | Print model summary | |

summary.gbm | Summary of a gbm object | |

quantile.rug | Quantile rug plot | |

test.gbm | Test the gbm package. | |

shrink.gbm | L1 shrinkage of the predictor variables in a GBM | |

shrink.gbm.pred | Predictions from a shrunked GBM | |

gbm | Generalized Boosted Regression Modeling (GBM) | |

gbm.fit | Generalized Boosted Regression Modeling (GBM) | |

gbm.more | Generalized Boosted Regression Modeling (GBM) | |

gbm.object | Generalized Boosted Regression Model Object | |

guessDist | gbm internal functions | |

gbm-package | Generalized Boosted Regression Models (GBMs) | |

gbm.perf | GBM performance | |

basehaz.gbm | Baseline hazard function | |

calibrate.plot | Calibration plot | |

No Results! |

## Vignettes of gbm

Name | ||

gbm.Rnw | ||

gbm.bib | ||

oobperf2.pdf | ||

shrinkage-v-iterations.pdf | ||

srcltx.sty | ||

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## Last month downloads

## Details

License | GPL (>= 2) | file LICENSE |

URL | https://github.com/gbm-developers/gbm |

BugReports | https://github.com/gbm-developers/gbm/issues |

RoxygenNote | 6.1.1 |

VignetteBuilder | knitr |

NeedsCompilation | yes |

Packaged | 2019-01-14 14:21:52 UTC; bgreenwell |

Repository | CRAN |

Date/Publication | 2019-01-14 15:00:03 UTC |

imports | gridExtra , lattice , parallel , survival |

suggests | knitr , pdp , RUnit , splines , viridis |

depends | R (>= 2.9.0) |

Contributors | Bradley Boehmke, Jay Cunningham, GBM Developers |

#### Include our badge in your README

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