varCounts.wsrf

object


  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.

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

Description

Usage

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See Also