# 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, the aggregation level for the output, and to perform
computations in parallel.`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

`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],UseParallel=true)`

specifies to use the first, second, third, and fifth learners in the
ensemble in `oobEdge`

, 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
`oobEdge`

, 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`

`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`

(default) | `true`

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 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
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 R2011a**

### R2022a: `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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