oobMeanMargin
Out-of-bag mean margins
Syntax
mar = oobMeanMargin(B)
mar = oobMeanMargin(B,'param1',val1,'param2',val2,...)
Description
mar = oobMeanMargin(B)
computes average
classification margins for out-of-bag observations in the training
data, using the trained bagger B
. oobMeanMargin
averages
the margins over all out-of-bag observations. mar
is
a row-vector of length NTrees
, where NTrees
is
the number of trees in the ensemble.
mar = oobMeanMargin(B,'param1',val1,'param2',val2,...)
specifies
optional parameter name/value pairs:
'Mode' | Character vector or string scalar indicating how oobMeanMargin computes
errors. If set to 'cumulative' (default), is a
vector of length NTrees where the first element
gives mean margin from trees(1) , second column
gives mean margins from trees(1:2) etc., up to
trees(1:NTrees) . If set to
'individual' , mar is a
vector of length NTrees , where each element is a
mean margin from each tree in the ensemble. If set to
'ensemble' , mar is a
scalar showing the cumulative mean margin 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
mean margin from trees(1) , the second element gives
mean margin from trees(1:2) etc. |
'TreeWeights' | Vector of tree weights. This vector must have the same length
as the 'Trees' vector. oobMeanMargin 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. |