mar = oobMeanMargin(B)
mar = oobMeanMargin(B,'param1',val1,'param2',val2,...)
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'||String indicating how oobMargin 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.|