print

print.wsrf

Print a summary of the forest model or one specific tree in the forest
 model built from <code>wsrf</code>.

A parallel implementation of Weighted Subspace Random Forest. The
Weighted Subspace Random Forest algorithm was proposed in the
International Journal of Data Warehousing and Mining by Baoxun Xu,
Joshua Zhexue Huang, Graham Williams, Qiang Wang, and Yunming Ye
(2012) <DOI:10.4018/jdwm.2012040103>. The algorithm can classify
very high-dimensional data with random forests built using small
subspaces. A novel variable weighting method is used for variable
subspace selection in place of the traditional random variable
sampling.This new approach is particularly useful in building
models from high-dimensional data.

He Zhao

wsrf

Weighted Subspace Random Forest for Classification

Qinghan Meng

Junchao Lv

Baoxun Xu

print.wsrf function

<dl>
 <dt>x</dt>
<dd>object of class <code>wsrf</code>.</dd>
 <dt>trees</dt>
<dd>the index of a specific tree. If missing, <code>print</code>
 will print a summary of the model.</dd>
 <dt>...</dt>
<dd>optional additional arguments. At present no additional
 arguments are used.</dd>
</dl>

Arguments

He Zhao and Graham Williams (SIAT, CAS)

Author

Print a summary of the forest model or one specific tree in the forest
 model built from <code>wsrf</code>.

Print Method for wsrf Model — print.wsrf

<dl>


 <dt>x</dt>
<dd>object of class <code>wsrf</code>.</dd>


 <dt>trees</dt>
<dd>the index of a specific tree. If missing, <code>print</code>
 will print a summary of the model.</dd>


 <dt>...</dt>
<dd>optional additional arguments. At present no additional
 arguments are used.</dd>


</dl>

print.wsrf: Print Method for `wsrf` Model

Description

Usage

Arguments

Author

See Also