b52db87d5c
* [gblinear] add features contribution prediction; fix DumpModel bug * [gbtree] minor changes to PredContrib * [R] add feature contribution prediction to R * [R] bump up version; update NEWS * [gblinear] fix the base_margin issue; fixes #1969 * [R] list of matrices as output of multiclass feature contributions * [gblinear] make order of DumpModel coefficients consistent: group index changes the fastest
41 lines
1.5 KiB
Plaintext
41 lines
1.5 KiB
Plaintext
Package: xgboost
|
|
Type: Package
|
|
Title: Extreme Gradient Boosting
|
|
Version: 0.6.4.5
|
|
Date: 2017-01-04
|
|
Author: Tianqi Chen <tianqi.tchen@gmail.com>, Tong He <hetong007@gmail.com>,
|
|
Michael Benesty <michael@benesty.fr>, Vadim Khotilovich <khotilovich@gmail.com>,
|
|
Yuan Tang <terrytangyuan@gmail.com>
|
|
Maintainer: Tong He <hetong007@gmail.com>
|
|
Description: Extreme Gradient Boosting, which is an efficient implementation
|
|
of the gradient boosting framework from Chen & Guestrin (2016) <doi:10.1145/2939672.2939785>.
|
|
This package is its R interface. The package includes efficient linear
|
|
model solver and tree learning algorithms. The package can automatically
|
|
do parallel computation on a single machine which could be more than 10
|
|
times faster than existing gradient boosting packages. It supports
|
|
various objective functions, including regression, classification and ranking.
|
|
The package is made to be extensible, so that users are also allowed to define
|
|
their own objectives easily.
|
|
License: Apache License (== 2.0) | file LICENSE
|
|
URL: https://github.com/dmlc/xgboost
|
|
BugReports: https://github.com/dmlc/xgboost/issues
|
|
VignetteBuilder: knitr
|
|
Suggests:
|
|
knitr,
|
|
rmarkdown,
|
|
ggplot2 (>= 1.0.1),
|
|
DiagrammeR (>= 0.9.0),
|
|
Ckmeans.1d.dp (>= 3.3.1),
|
|
vcd (>= 1.3),
|
|
testthat,
|
|
igraph (>= 1.0.1)
|
|
Depends:
|
|
R (>= 3.3.0)
|
|
Imports:
|
|
Matrix (>= 1.1-0),
|
|
methods,
|
|
data.table (>= 1.9.6),
|
|
magrittr (>= 1.5),
|
|
stringi (>= 0.5.2)
|
|
RoxygenNote: 6.0.1
|