variable.names.xgb.Booster

Returns the feature / variable / column names from a fitted
booster object, which are set automatically during the call to <code>xgb.train()</code>
from the DMatrix names, or which can be set manually through <code>setinfo()</code>.
If the object doesn't have feature names, will return <code>NULL</code>.
It is equivalent to calling <code>getinfo(object, "feature_name")</code>.

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.

Jiaming Yuan

xgboost

Extreme Gradient Boosting

Tianqi Chen

Tong He

Michael Benesty

Vadim Khotilovich

Yuan Tang

Hyunsu Cho

Kailong Chen

Rory Mitchell

Ignacio Cano

Tianyi Zhou

Mu Li

Junyuan Xie

Min Lin

Yifeng Geng

Yutian Li

David Cortes

XGBoost contributors 

variable.names.xgb.Booster function

<dl><dt>object</dt>
<dd>An <code>xgb.Booster</code> object.</dd>
<dt>...</dt>
<dd>Not used.</dd></dl>

Arguments

Get Features Names from Booster — variable.names.xgb.Booster

<dl>

<dt>object</dt>
<dd>An <code>xgb.Booster</code> object.</dd>


<dt>...</dt>
<dd>Not used.</dd>

</dl>

Get Features Names from Booster

variable.names.xgb.Booster: Get Features Names from Booster

Description

Usage

Arguments