I try to figure out how the classification score is calculated when using cecision trees with the fitcensemble function. In my opinion, the following link is ambiguous:
First, it is said that the score is equal to the following:
"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"
Furthermore, the first link also provides the following definition: "Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type."
So could anyone explain what the real definition of score is when using fitcensemble with Decision Trees (does it depend on Boosting or Bagging?)
Thanks for your help!