normalize

This is used to compare multiple features on the same plot.
Internal utility function

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

XGBoost contributors 

normalize function

<dl><dt>x</dt>
<dd>Numeric vector</dd></dl>

Arguments

Scale feature value to have mean 0, standard deviation 1 — normalize

<dl>

<dt>x</dt>
<dd>Numeric vector</dd>

</dl>

Scale feature value to have mean 0, standard deviation 1

normalize: Scale feature value to have mean 0, standard deviation 1

Description

Usage

Value

Arguments