mar = meanMargin(B,X,Y)
mar = meanMargin(B,X,Y,'param1',val1,'param2',val2,...)
mar = meanMargin(B,X,Y)
computes average
classification margins for predictors X
given true
response Y
. The Y
can be either
a numeric vector, character matrix, cell array of strings, categorical
vector or logical vector. meanMargin
averages the
margins over all observations (rows) in X
for each
tree. mar
is a matrix of size 1byNTrees
,
where NTrees
is the number of trees in the ensemble B
.
This method is available for classification ensembles only.
mar = meanMargin(B,X,Y,'param1',val1,'param2',val2,...)
specifies
optional parameter name/value pairs:
'Mode'  String indicating how meanMargin 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. meanMargin 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. 