Out-of-bag margins
Syntax
mar = oobMargin(B)
mar = oobMargin(B,'param1',val1,'param2',val2,...)
Description
mar = oobMargin(B) computes an Nobs-by-NTrees matrix
of classification margins for out-of-bag observations in the training
data, using the trained bagger B.
mar = oobMargin(B,'param1',val1,'param2',val2,...) specifies
optional parameter name/value pairs:
| 'mode' | String indicating how oobMargin computes
errors. If set to 'cumulative' (default), the method
computes cumulative margins and mar is an Nobs-by-NTrees matrix,
where the first column gives margins from trees(1),
second column gives margins from trees(1:2) etc,
up to trees(1:NTrees). If set to 'individual', mar is
an Nobs-by-NTrees matrix, where
each column gives margins from each tree in the ensemble. If set to 'ensemble', mar is
a single column of length Nobs showing the cumulative
margins 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 an Nobs-by-NTrees matrix
for 'cumulative' and 'individual' modes,
where NTrees is the number of elements in the input
vector, and a single column for 'ensemble' mode.
For example, in the 'cumulative' mode, the first
column gives margins from trees(1), the second
column gives margins from trees(1:2) etc. |
| 'treeweights' | Vector of tree weights. This vector must have the same length
as the 'trees' vector. oobMargin 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.margin
 | OOBInstanceWeight property (TreeBagger) | | oobMeanMargin (TreeBagger) |  |
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