edge = oobEdge(ens)
edge = oobEdge(ens,Name,Value)
classification edge with additional options specified by one or more
edge = oobEdge(
arguments. You can specify several name-value pair arguments in any
A classification bagged ensemble, constructed with
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 in the ensemble ranging from
Character vector representing the meaning of the output
Classification edge, a weighted average of the classification margin.
Load Fisher's iris data set.
Train an ensemble of 100 bagged classification trees using the entire data set.
rng(1) % For reproducibility Mdl = fitensemble(meas,species,'Bag',100,'Tree','type','classification');
Estimate the out-of-bag edge.
edge = oobEdge(Mdl)
edge = 0.8731
The edge is the weighted mean value of
the classification margin. The weights are the class probabilities
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
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,
many bootstrap replicas of the dataset and grows decision trees on
fitensemble obtains each bootstrap
replica by randomly selecting
N observations out
N with replacement, where
the dataset size. To find the predicted response of a trained ensemble,
an average over predictions from individual trees.
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