oob.error.rate

oob.error.rate.wsrf

object

logical or an integer vector for the index of a specific
      tree in the forest model.  If provided as an integer vector,
      <code>oobErrorRate.wsrf</code> will give the corresponding out-of-bag
      error rates of the exact trees specified by <code>tree</code>.  If
      TRUE, all error rates will be presented.  If FALSE or missing,
      the gross error rate for the forest will be given.

tree


  Return out-of-bag error rate for "<code>wsrf</code>" model.


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.

oob.error.rate.wsrf: Out-of-Bag Error Rate

Description

Usage

Arguments

Value

See Also