Class: CompactTreeBagger

Error (misclassification probability or MSE)


err = error(B,X,Y)
err = error(B,X,Y,'param1',val1,'param2',val2,...)


err = error(B,X,Y) computes the misclassification probability (for classification trees) or mean squared error (MSE, for regression trees) for each tree, for predictors X given true response Y. For classification, Y can be either a numeric vector, character matrix, cell array of strings, categorical vector or logical vector. For regression, Y must be a numeric vector. err is a vector with one error measure for each of the NTrees trees in the ensemble B.

err = error(B,X,Y,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:

'mode'String indicating how the method computes errors. If set to 'cumulative' (default), error computes cumulative errors and err is a vector of length NTrees, where the first element gives error from trees(1), second element gives error fromtrees(1:2) etc, up to trees(1:NTrees). If set to 'individual', err is a vector of length NTrees, where each element is an error from each tree in the ensemble. If set to 'ensemble', err is a scalar showing the cumulative error 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 error from trees(1), the second element gives error from trees(1:2) etc.
'treeweights'Vector of tree weights. This vector must have the same length as the 'trees' vector. The method uses these weights to combine output from the specified trees by taking a weighted average instead of the simple non-weighted majority vote. You cannot use this argument in the 'individual' mode.
'useifort'Logical matrix of size Nobs-by-NTrees indicating which trees should be used to make predictions for each observation. By default the method uses all trees for all observations.

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