labels = predict(ens,X)
[labels,score] = predict(ens,X)
[labels,...] = predict(ens,X,Name,Value)
A matrix where each row represents an observation, and each
column represents a predictor. The number of columns in
Specify optional comma-separated pairs of
Name is the argument
Value is the corresponding
Name must appear
inside single quotes (
You can specify several name and value pair
arguments in any order as
Indices of weak learners
A logical matrix of size
Vector of classification labels.
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.
For ensembles, a classification score represents the confidence of a classification into a class. The higher the score, the higher the confidence.
Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:
AdaBoostM1 scores range from –∞
Bag scores range from
Train a boosting ensemble for the
and predict the classification of the mean of the data:
load ionosphere; ada = fitensemble(X,Y,'AdaBoostM1',100,'tree'); Xbar = mean(X); [ypredict score] = predict(ada,Xbar) ypredict = 'g' score = -2.9460 2.9460