Documentation

meanMargin

Class: TreeBagger

Mean classification margin

Syntax

mar = meanMargin(B,TBLnew,Ynew)
mar = meanMargin(B,Xnew,Ynew)
mar = meanMargin(B,TBLnew,Ynew,'param1',val1,'param2',val2,...)
mar = meanMargin(B,Xnew,Ynew,'param1',val1,'param2',val2,...)

Description

mar = meanMargin(B,TBLnew,Ynew) computes average classification margins for the predictors contained in the table TBLnew given the true response Ynew. You can omit Ynew if TBLnew contains the response variable. If you trained B using sample data contained in a table, then the input data for this method must also be in a table.

mar = meanMargin(B,Xnew,Ynew) computes average classification margins for the predictors contained in the matrix Xnew given true response Ynew. If you trained B using sample data contained in a matrix, then the input data for this method must also be in a matrix.

Ynew 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 TBLnew or Xnew for each tree. mar is a matrix of size 1-by-NTrees, where NTrees is the number of trees in the ensemble B. This method is available for classification ensembles only.

mar = meanMargin(B,TBLnew,Ynew,'param1',val1,'param2',val2,...) or mar = meanMargin(B,Xnew,Ynew,'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.

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