prepare.ggplot.shap.data

Conforms to data format required for ggplot functions.

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 

prepare.ggplot.shap.data function

<dl><dt>data_list</dt>
<dd>List containing 'data' and 'shap_contrib' returned by
<code>xgb.shap.data()</code>.</dd>
<dt>normalize</dt>
<dd>Whether to standardize feature values to have mean 0 and
standard deviation 1 (useful for comparing multiple features on the same
plot). Default <code>FALSE</code>.</dd></dl>

Arguments

Combine and melt feature values and SHAP contributions for sample
observations. — prepare.ggplot.shap.data

<dl>

<dt>data_list</dt>
<dd>List containing 'data' and 'shap_contrib' returned by
<code>xgb.shap.data()</code>.</dd>


<dt>normalize</dt>
<dd>Whether to standardize feature values to have mean 0 and
standard deviation 1 (useful for comparing multiple features on the same
plot). Default <code>FALSE</code>.</dd>

</dl>

Combine and melt feature values and SHAP contributions for sample
observations.

prepare.ggplot.shap.data: Combine and melt feature values and SHAP contributions for sample observations.

Description

Usage

Value

Arguments

Details