# resubEdge

Resubstitution classification edge for classification ensemble model

## Description

returns the resubstitution Classification Edge
(`edge`

= resubEdge(`ens`

)`edge`

) for the trained classification ensemble
model `ens`

using the training data stored in
`ens.X`

and the corresponding true class
labels stored in `ens.Y`

. The classification edge
is the Classification Margin averaged over the entire
data set. `edge`

can be a scalar or vector,
depending on the setting of the `Mode`

name-value
argument.

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 loss, select the aggregation level for the
output, and perform computations in parallel.`edge`

= resubEdge(`ens`

,`Name=Value`

)

## Examples

### Find Classification Edge by Resubstitution of Training Data

Find the resubstitution edge for an ensemble that classifies the Fisher iris data.

Load the sample data set.

`load fisheriris`

Train an ensemble of 100 boosted classification trees using AdaBoostM2.

t = templateTree(MaxNumSplits=1); % Weak learner template tree object ens = fitcensemble(meas,species,"Method","AdaBoostM2","Learners",t);

Find the resubstitution edge.

edge = resubEdge(ens)

edge = 3.2486

## Input Arguments

`ens`

— Classification ensemble model

`ClassificationEnsemble`

model object

Classification ensemble model, specified as a `ClassificationEnsemble`

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: **```
resubEdge(ens,Learners=[1 2 3
5],UseParallel=true)
```

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

, 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
`resubEdge`

, 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`

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
`resubEdge`

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

### Classification Edge

The *classification edge* is the weighted mean
value of the classification margin. The weights are the class
probabilities in
`ens`

`.Prior`

.

### 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`

.

### Score (ensemble)

For ensembles, a classification *score* represents
the confidence of a classification into a class. The higher the score,
the higher the confidence.

Different ensemble algorithms have different definitions for their scores. Furthermore, the range of scores depends on ensemble type. For example:

`AdaBoostM1`

scores range from –∞ to ∞.`Bag`

scores range from`0`

to`1`

.

## 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).

You cannot use `UseParallel`

with GPU arrays.

### GPU Arrays

Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

You cannot use

`UseParallel`

with GPU arrays.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

## Version History

**Introduced in R2011a**

### R2023b: Observations with missing predictor values are used in resubstitution and cross-validation computations

Starting in R2023b, the following classification model object functions use observations with missing predictor values as part of resubstitution ("resub") and cross-validation ("kfold") computations for classification edges, losses, margins, and predictions.

In previous releases, the software omitted observations with missing predictor values from the resubstitution and cross-validation computations.

### R2022a: `resubEdge`

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 `resubEdge`

function returns a different value compared to previous releases.

The `resubEdge`

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 changes, 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

`resubMargin`

| `resubLoss`

| `resubPredict`

| `resubEdge`

| `ClassificationEnsemble`

| `fitcensemble`

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