oobEdge

The edge is the weighted mean value of the classification margin. The weights are the class probabilities in ens.Prior.

The classification margin is the difference between the classification score for the true class and maximal classification score for the false classes. margin is a column vector with the same number of rows as in the matrix ens.X.

Bagging, which stands for “bootstrap aggregation,”, is a type of ensemble learning. To bag a weak learner such as a decision tree on a data set, fitcensemble generates many bootstrap replicas of the data set and grows decision trees on these replicas. fitcensemble obtains each bootstrap replica by randomly selecting N observations out of N with replacement, where N is the data set size. To find the predicted response of a trained ensemble, predict take an average over predictions from individual trees.

Drawing N out of N observations with replacement omits on average 37% (1/e) of observations for each decision tree. These are "out-of-bag" observations. For each observation, oobLoss estimates the out-of-bag prediction by averaging over predictions from all trees in the ensemble for which this observation is out of bag. It then compares the computed prediction against the true response for this observation. It calculates the out-of-bag error by comparing the out-of-bag predicted responses against the true responses for all observations used for training. This out-of-bag average is an unbiased estimator of the true ensemble error.