Out-of-bag classification margins
margin = oobMargin(ens)
margin = oobMargin(ens,Name,Value)
A classification bagged ensemble, constructed with
Specify optional pairs of arguments as
the argument name and
Value is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Before R2021a, use commas to separate each name and value, and enclose
Name in quotes.
Learners — Indices of weak learners
[1:ens.NumTrained] (default) | vector of positive integers
Indices of weak learners in the ensemble to use in
oobMargin, specified as a vector of positive integers in the range
ens.NumTrained]. By default, all learners are used.
Learners=[1 2 4]
Indication to perform inference in parallel, specified as
true (compute in parallel). Parallel computation
requires Parallel Computing Toolbox™. Parallel inference can be faster than serial inference, especially for
large datasets. Parallel computation is supported only for tree learners.
A numeric column vector of length
Find Out-of-Bag Classification Margins
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 = 109
Out of Bag
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
Automatic Parallel Support
Accelerate code by automatically running computation in parallel using Parallel Computing Toolbox™.
To run in parallel, set the
UseParallel name-value argument to
true in the call to this function.
For more general information about parallel computing, see Run MATLAB Functions with Automatic Parallel Support (Parallel Computing Toolbox).