oobEdge
Out-of-bag classification edge for bagged classification ensemble model
Description
returns the classification
edge
e
= oobEdge(ens
)e
for the out-of-bag data in the bagged classification
ensemble model 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 edge, and the aggregation level for the output.e
= oobEdge(ens
,Name=Value
)
Examples
Estimate Out-of-Bag Edge
Load Fisher's iris data set.
load fisheriris
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
Input Arguments
ens
— Bagged classification ensemble model
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: oobEdge(ens,Learners=[1 2 3 5])
specifies to use
the first, second, third, and fifth learners in the ensemble
ens
.
Learners
— Indices of weak learners
[1:ens.NumTrained]
(default) | vector of positive integers
Indices of the weak learners in the ensemble to use with
oobEdge
, specified as a
vector of positive integers in the range
[1:ens.NumTrained
]. By default,
the function uses all learners.
Example: Learners=[1 2 4]
Data Types: single
| double
Mode
— Aggregation level for output
"ensemble"
(default) | "individual"
| "cumulative"
Aggregation level for the output, specified as "ensemble"
,
"individual"
, or "cumulative"
.
Value | Description |
---|---|
"ensemble" | The output is a scalar value, the loss for the entire ensemble. |
"individual" | The output is a vector with one element per trained learner. |
"cumulative" | The output is a vector in which element J is
obtained by using learners 1:J from the input
list of learners. |
Example: Mode="individual"
Data Types: char
| string
UseParallel
— Flag to run in parallel
false
or 0
(default) | true
or 1
Flag to run in parallel, specified as a numeric or logical
1
(true
) or 0
(false
). If you specify UseParallel=true
, the
oobEdge
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
Edge
The edge is the weighted mean value of the
classification margin. The weights are the class probabilities in
ens
.Prior
.
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
.
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 data set,
fitcensemble
generates many bootstrap
replicas of the data set 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 data set size. To find the predicted response of a trained
ensemble, predict
take 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.
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 R2011aR2022a: oobEdge
returns a different value for a model with a nondefault cost matrix
If you specify a nondefault cost matrix when you train the input model object, the oobEdge
function returns a different value compared to previous releases.
The oobEdge
function uses the
observation weights stored in the W
property. The way the function uses the
W
property value has not changed. However, the property value stored in the input model object has changed for a
model with a nondefault cost matrix, so the function might return a different value.
For details about the property value change, see Cost property stores the user-specified cost matrix.
If you want the software to handle the cost matrix, prior
probabilities, and observation weights in the same way as in previous releases, adjust the prior
probabilities and observation weights for the nondefault cost matrix, as described in Adjust Prior Probabilities and Observation Weights for Misclassification Cost Matrix. Then, when you train a
classification model, specify the adjusted prior probabilities and observation weights by using
the Prior
and Weights
name-value arguments, respectively,
and use the default cost matrix.
See Also
oobMargin
| oobPredict
| oobLoss
| ClassificationBaggedEnsemble
| fitcensemble
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