Out-of-bag classification margins
margin = oobMargin(ens)
margin = oobMargin(ens,Name,Value)
A classification bagged ensemble, constructed with
comma-separated pairs of
the argument name and
Value is the corresponding value.
Name must appear inside quotes. You can specify several name and value
pair arguments in any order as
Indices of weak learners in the ensemble ranging from
A numeric column vector of length
Find the out-of-bag margins for a bagged ensemble from the Fisher iris data.
Load the sample data set.
Train an ensemble of bagged classification trees.
ens = fitcensemble(meas,species,'Method','Bag');
Find the number of out-of-bag margins that are equal to
margin = oobMargin(ens); sum(margin == 1)
ans = 118
Bagging, which stands for “bootstrap aggregation”, is a
type of ensemble learning. To bag a weak learner such as a decision tree on a dataset,
fitrensemble generates many bootstrap
replicas of the dataset and grows decision trees on these replicas.
fitrensemble obtains each bootstrap replica by randomly selecting
N observations out of
N with replacement, where
N is the dataset size. To find the predicted response of a trained
predict takes an average over predictions from
N out of
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
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