oobMargin
Out-of-bag classification margins of bagged classification ensemble
Description
returns the classification margins
for the out-of-bag data in the bagged classification ensemble model
m
= oobMargin(ens
)ens
.
specifies additional options using one or more name-value arguments. For example,
you can specify the indices of the weak learners to use for calculating the margins,
and perform computations in parallel.m
= oobMargin(ens
,Name=Value
)
Examples
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.
load fisheriris
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 1
.
margin = oobMargin(ens); sum(margin == 1)
ans = 109
Input Arguments
ens
— Bagged classification ensemble
ClassificationBaggedEnsemble
model object
Bagged classification ensemble model, specified as a ClassificationBaggedEnsemble
model object trained with fitcensemble
.
Name-Value Arguments
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
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.
Example: oobMargin(ens,Learners=[1 2 3 5],UseParallel=true)
specifies to use the first, second, third, and fifth learners in the ensemble in
oobMargin
, and to perform computations in
parallel.
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
[1:ens.NumTrained
]. By default, all learners are used.
Example: Learners=[1 2 4]
Data Types: single
| double
UseParallel
— Flag to run in parallel
false
(default) | true
Flag to run in parallel, specified as a numeric or logical 1
(true
) or 0 (false
). If you specify
UseParallel=true
, the oobMargin
function executes
for
-loop iterations by using parfor
. The loop runs in parallel when you have Parallel Computing Toolbox™.
Example: UseParallel=true
Data Types: logical
More About
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
ensemble, predict
takes an average over predictions from
individual trees.
Drawing N
out of N
observations
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
error.
Classification Margin
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
ens
.X
.
Extended Capabilities
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).
Version History
Introduced in R2012b
See Also
oobPredict
| oobLoss
| oobEdge
| margin
| ClassificationBaggedEnsemble
| fitcensemble
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