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. |
See Also
CompactTreeBagger.error
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