oobMargin
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' | Character vector or string scalar 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. |