Documentation

oobError

Class: TreeBagger

Out-of-bag error

Syntax

err = oobError(B)
err = oobError(B,'param1',val1,'param2',val2,...)

Description

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.

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