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YFIT = predict(B,X)
[YFIT,stdevs] = predict(B,X)
[YFIT,scores] = predict(B,X)
[YFIT,scores,stdevs] = predict(B,X)
Y = predict(B,X,'param1',val1,'param2',val2,...)
YFIT = predict(B,X) computes the predicted response of the trained ensemble B for predictors X. By default, predict takes a democratic (nonweighted) average vote from all trees in the ensemble. In X, rows represent observations and columns represent variables. YFIT is a cell array of strings for classification and a numeric array for regression.
For regression, [YFIT,stdevs] = predict(B,X) also returns standard deviations of the computed responses over the ensemble of the grown trees.
For classification, [YFIT,scores] = predict(B,X) returns scores for all classes. scores is a matrix with one row per observation and one column per class. For each observation and each class, the score generated by each tree is the probability of this observation originating from this class computed as the fraction of observations of this class in a tree leaf. predict averages these scores over all trees in the ensemble.
[YFIT,scores,stdevs] = predict(B,X)also returns standard deviations of the computed scores for classification. stdevs is a matrix with one row per observation and one column per class, with standard deviations taken over the ensemble of the grown trees.
Y = predict(B,X,'param1',val1,'param2',val2,...) specifies optional parameter name/value pairs:
'trees' | Array of tree indices to use for computation of responses. Default is 'all'. |
'treeweights' | Array of NTrees weights for weighting votes from the specified trees. |
'useifort' | Logical matrix of size Nobs-by-NTrees indicating which trees to use to make predictions for each observation. By default all trees are used for all observations. |