varCounts.wsrf

Return the times of each variable being selected as split condition.
 For evaluating the bias of wsrf towards attribute types (categorical and
 numerical) and the number of values each attribute has.

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

varCounts.wsrf function

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

Arguments

He Zhao and Graham Williams (SIAT, CAS)

Author

Number of Times of Variables Selected as Split Condition — varCounts.wsrf

<dl>

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

</dl>

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varCounts.wsrf: Number of Times of Variables Selected as Split Condition

Description

Usage

Value

Arguments

Author

See Also

Join us for RADAR: AI Edition

Description

Usage

Value

Arguments

Author

See Also

Join us for
RADAR: AI Edition