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.|