err = oobError(B)
err = oobError(B,'param1',val1,'param2',val2,...)
err = oobError(B) computes the misclassification probability (for classification trees) or mean squared error (for regression trees) for out-of-bag observations in the training data, using the trained bagger B. err is a vector of length NTrees, where NTrees is the number of trees in the ensemble.
err = oobError(B,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:
|'mode'||String indicating how oobError computes errors. If set to 'cumulative' (default), the method computes cumulative errors and err is a vector of length NTrees, where the first element gives error from trees(1), second element gives error from trees(1:2) etc, up to trees(1:NTrees). If set to 'individual', err is a vector of length NTrees, where each element is an error from each tree in the ensemble. If set to 'ensemble', err is a scalar showing the cumulative error for the entire ensemble.|
|'trees'||Vector of indices indicating what trees to include in this calculation. By default, this argument is set to 'all' and the method uses all trees. If 'trees' is a numeric vector, the method returns a vector of length NTrees for 'cumulative' and 'individual' modes, where NTrees is the number of elements in the input vector, and a scalar for 'ensemble' mode. For example, in the 'cumulative' mode, the first element gives error from trees(1), the second element gives error from trees(1:2) etc.|
|'treeweights'||Vector of tree weights. This vector must have the same length as the 'trees' vector. oobError uses these weights to combine output from the specified trees by taking a weighted average instead of the simple nonweighted majority vote. You cannot use this argument in the 'individual' mode.|