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 

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, 8(2):44-63, 2012, proposed by Baoxun Xu, Joshua Zhexue
    Huang, Graham Williams, Qiang Wang, and Yunming Ye.  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

concept

See Also