Out-of-bag classification edge
edge = oobEdge(ens)
edge = oobEdge(ens,Name,Value)
returns out-of-bag classification edge for
edge = oobEdge(
computes classification edge with additional
options specified by one or more
edge = oobEdge(
Name,Value pair arguments.
You can specify several name-value pair arguments
in any order as
A classification bagged ensemble,
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.
Indices of weak learners in the ensemble ranging from
Character vector or string scalar
representing the meaning of the output
Indication to perform inference in parallel, specified as
Classification edge, a weighted average of the classification margin.
Estimate Out-of-Bag Edge
Load Fisher's iris data set.
Train an ensemble of 100 bagged classification trees using the entire data set.
Mdl = fitcensemble(meas,species,'Method','Bag');
Estimate the out-of-bag edge.
edge = oobEdge(Mdl)
edge = 0.8767
The edge is the
weighted mean value of the classification margin.
The weights are the class probabilities in
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
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,
fitcensemble generates many bootstrap
replicas of the dataset and grows decision trees on these replicas.
fitcensemble 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 take 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
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).