xgboost/R-package
Jiaming Yuan e7ac2486eb
[backport] [R] Fix global feature importance and predict with 1 sample. (#7394) (#7397)
* [R] Fix global feature importance.

* Add implementation for tree index.  The parameter is not documented in C API since we
should work on porting the model slicing to R instead of supporting more use of tree
index.

* Fix the difference between "gain" and "total_gain".

* debug.

* Fix prediction.
2021-11-06 00:07:36 +08:00
..
2015-01-20 15:51:42 -08:00
2021-05-11 20:44:36 +08:00
2015-07-24 11:58:02 -07:00
2021-10-15 12:21:04 +08:00
2021-03-30 22:27:30 +08:00

XGBoost R Package for Scalable GBM

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Installation

We are on CRAN now. For stable/pre-compiled(for Windows and OS X) version, please install from CRAN:

install.packages('xgboost')

For more detailed installation instructions, please see here.

Examples

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