# edge

**Class: **ClassificationLinear

Classification edge for linear classification models

## Description

returns the classification edges for the trained linear classifier
`e`

= edge(`Mdl`

,`Tbl`

,`ResponseVarName`

)`Mdl`

using the predictor data in
`Tbl`

and the class labels in
`Tbl.ResponseVarName`

.

specifies options using one or more name-value pair arguments in addition to any
of the input argument combinations in previous syntaxes. For example, you can
specify that columns in the predictor data correspond to observations or supply
observation weights.`e`

= edge(___,`Name,Value`

)

**Note**

If the predictor data `X`

or the predictor variables in
`Tbl`

contain any missing values, the
`edge`

function can return NaN. For more
details, see edge can return NaN for predictor data with missing values.

## Input Arguments

`Mdl`

— Binary, linear classification model

`ClassificationLinear`

model object

Binary, linear classification model, specified as a `ClassificationLinear`

model object.
You can create a `ClassificationLinear`

model object
using `fitclinear`

.

`X`

— Predictor data

full matrix | sparse matrix

Predictor data, specified as an *n*-by-*p* full or sparse matrix. This orientation of `X`

indicates that rows correspond to individual observations, and columns correspond to individual predictor variables.

**Note**

If you orient your predictor matrix so that observations correspond to columns and specify `'ObservationsIn','columns'`

, then you might experience a significant reduction in computation time.

The length of `Y`

and the number of observations
in `X`

must be equal.

**Data Types: **`single`

| `double`

`Y`

— Class labels

categorical array | character array | string array | logical vector | numeric vector | cell array of character vectors

Class labels, specified as a categorical, character, or string array; logical or numeric vector; or cell array of character vectors.

The data type of

`Y`

must be the same as the data type of`Mdl.ClassNames`

. (The software treats string arrays as cell arrays of character vectors.)The distinct classes in

`Y`

must be a subset of`Mdl.ClassNames`

.If

`Y`

is a character array, then each element must correspond to one row of the array.The length of

`Y`

must be equal to the number of observations in`X`

or`Tbl`

.

**Data Types: **`categorical`

| `char`

| `string`

| `logical`

| `single`

| `double`

| `cell`

`Tbl`

— Sample data

table

Sample data used to train the model, specified as a table. Each row of
`Tbl`

corresponds to one observation, and each column corresponds
to one predictor variable. Optionally, `Tbl`

can contain additional
columns for the response variable and observation weights. `Tbl`

must
contain all the predictors used to train `Mdl`

. Multicolumn variables
and cell arrays other than cell arrays of character vectors are not allowed.

If `Tbl`

contains the response variable used to train `Mdl`

, then you do not need to specify `ResponseVarName`

or `Y`

.

If you train `Mdl`

using sample data contained in a table, then the input
data for `edge`

must also be in a table.

`ResponseVarName`

— Response variable name

name of variable in `Tbl`

Response variable name, specified as the name of a variable in `Tbl`

. If `Tbl`

contains the response variable used to train `Mdl`

, then you do not need to specify `ResponseVarName`

.

If you specify `ResponseVarName`

, then you must specify it as a character
vector or string scalar. For example, if the response variable is stored as
`Tbl.Y`

, then specify `ResponseVarName`

as
`'Y'`

. Otherwise, the software treats all columns of
`Tbl`

, including `Tbl.Y`

, as predictors.

The response variable must be a categorical, character, or string array; a logical or numeric vector; or a cell array of character vectors. If the response variable is a character array, then each element must correspond to one row of the array.

**Data Types: **`char`

| `string`

### 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.*

`ObservationsIn`

— Predictor data observation dimension

`'rows'`

(default) | `'columns'`

Predictor data observation dimension, specified as `'rows'`

or
`'columns'`

.

**Note**

If you orient your predictor matrix so that observations correspond to columns and
specify `'ObservationsIn','columns'`

, then you might experience a
significant reduction in computation time. You cannot specify
`'ObservationsIn','columns'`

for predictor data in a
table.

**Data Types: **`char`

| `string`

`Weights`

— Observation weights

`ones(size(X,1),1)`

(default) | numeric vector | name of variable in `Tbl`

Observation weights, specified as the comma-separated pair consisting of
`'Weights'`

and a numeric vector or the name of a
variable in `Tbl`

.

If you specify

`Weights`

as a numeric vector, then the size of`Weights`

must be equal to the number of observations in`X`

or`Tbl`

.If you specify

`Weights`

as the name of a variable in`Tbl`

, then the name must be a character vector or string scalar. For example, if the weights are stored as`Tbl.W`

, then specify`Weights`

as`'W'`

. Otherwise, the software treats all columns of`Tbl`

, including`Tbl.W`

, as predictors.

If you supply weights, then for each regularization strength,
`edge`

computes the weighted classification
edge and normalizes weights to sum up to the value of the
prior probability in the respective class.

**Data Types: **`double`

| `single`

## Output Arguments

`e`

— Classification edges

numeric scalar | numeric row vector

Classification edges, returned as a numeric scalar or row vector.

`e`

is the same size as `Mdl.Lambda`

. `e(`

is
the classification edge of the linear classification model trained
using the regularization strength * j*)

`Mdl.Lambda(``j`

)

.## Examples

### Estimate Test-Sample Edge

Load the NLP data set.

`load nlpdata`

`X`

is a sparse matrix of predictor data, and `Y`

is a categorical vector of class labels. There are more than two classes in the data.

The models should identify whether the word counts in a web page are from the Statistics and Machine Learning Toolbox™ documentation. So, identify the labels that correspond to the Statistics and Machine Learning Toolbox™ documentation web pages.

`Ystats = Y == 'stats';`

Train a binary, linear classification model that can identify whether the word counts in a documentation web page are from the Statistics and Machine Learning Toolbox™ documentation. Specify to holdout 30% of the observations. Optimize the objective function using SpaRSA.

rng(1); % For reproducibility CVMdl = fitclinear(X,Ystats,'Solver','sparsa','Holdout',0.30); CMdl = CVMdl.Trained{1};

`CVMdl`

is a `ClassificationPartitionedLinear`

model. It contains the property `Trained`

, which is a 1-by-1 cell array holding a `ClassificationLinear`

model that the software trained using the training set.

Extract the training and test data from the partition definition.

trainIdx = training(CVMdl.Partition); testIdx = test(CVMdl.Partition);

Estimate the training- and test-sample edges.

eTrain = edge(CMdl,X(trainIdx,:),Ystats(trainIdx))

eTrain = 15.6660

eTest = edge(CMdl,X(testIdx,:),Ystats(testIdx))

eTest = 15.4767

### Feature Selection Using Test-Sample Edges

One way to perform feature selection is to compare test-sample edges from multiple models. Based solely on this criterion, the classifier with the highest edge is the best classifier.

Load the NLP data set.

`load nlpdata`

`X`

is a sparse matrix of predictor data, and `Y`

is a categorical vector of class labels. There are more than two classes in the data.

The models should identify whether the word counts in a web page are from the Statistics and Machine Learning Toolbox™ documentation. So, identify the labels that correspond to the Statistics and Machine Learning Toolbox™ documentation web pages. For quicker execution time, orient the predictor data so that individual observations correspond to columns.

Ystats = Y == 'stats'; X = X'; rng(1); % For reproducibility

Create a data partition which holds out 30% of the observations for testing.

Partition = cvpartition(Ystats,'Holdout',0.30); testIdx = test(Partition); % Test-set indices XTest = X(:,testIdx); YTest = Ystats(testIdx);

`Partition`

is a `cvpartition`

object that defines the data set partition.

Randomly choose half of the predictor variables.

```
p = size(X,1); % Number of predictors
idxPart = randsample(p,ceil(0.5*p));
```

Train two binary, linear classification models: one that uses the all of the predictors and one that uses half of the predictors. Optimize the objective function using SpaRSA, and indicate that observations correspond to columns.

CVMdl = fitclinear(X,Ystats,'CVPartition',Partition,'Solver','sparsa',... 'ObservationsIn','columns'); PCVMdl = fitclinear(X(idxPart,:),Ystats,'CVPartition',Partition,'Solver','sparsa',... 'ObservationsIn','columns');

`CVMdl`

and `PCVMdl`

are `ClassificationPartitionedLinear`

models.

Extract the trained `ClassificationLinear`

models from the cross-validated models.

CMdl = CVMdl.Trained{1}; PCMdl = PCVMdl.Trained{1};

Estimate the test sample edge for each classifier.

fullEdge = edge(CMdl,XTest,YTest,'ObservationsIn','columns')

fullEdge = 15.4767

partEdge = edge(PCMdl,XTest(idxPart,:),YTest,'ObservationsIn','columns')

partEdge = 13.4458

Based on the test-sample edges, the classifier that uses all of the predictors is the better model.

### Find Good Lasso Penalty Using Edge

To determine a good lasso-penalty strength for a linear classification model that uses a logistic regression learner, compare test-sample edges.

Load the NLP data set. Preprocess the data as in Feature Selection Using Test-Sample Edges.

load nlpdata Ystats = Y == 'stats'; X = X'; Partition = cvpartition(Ystats,'Holdout',0.30); testIdx = test(Partition); XTest = X(:,testIdx); YTest = Ystats(testIdx);

Create a set of 11 logarithmically-spaced regularization strengths from $$1{0}^{-8}$$ through $$1{0}^{1}$$.

Lambda = logspace(-8,1,11);

Train binary, linear classification models that use each of the regularization strengths. Optimize the objective function using SpaRSA. Lower the tolerance on the gradient of the objective function to `1e-8`

.

rng(10); % For reproducibility CVMdl = fitclinear(X,Ystats,'ObservationsIn','columns',... 'CVPartition',Partition,'Learner','logistic','Solver','sparsa',... 'Regularization','lasso','Lambda',Lambda,'GradientTolerance',1e-8)

CVMdl = ClassificationPartitionedLinear CrossValidatedModel: 'Linear' ResponseName: 'Y' NumObservations: 31572 KFold: 1 Partition: [1x1 cvpartition] ClassNames: [0 1] ScoreTransform: 'none' Properties, Methods

Extract the trained linear classification model.

Mdl = CVMdl.Trained{1}

Mdl = ClassificationLinear ResponseName: 'Y' ClassNames: [0 1] ScoreTransform: 'logit' Beta: [34023x11 double] Bias: [-11.4326 -11.4326 -11.4326 -11.4326 -11.4326 ... ] Lambda: [1.0000e-08 7.9433e-08 6.3096e-07 5.0119e-06 ... ] Learner: 'logistic' Properties, Methods

`Mdl`

is a `ClassificationLinear`

model object. Because `Lambda`

is a sequence of regularization strengths, you can think of `Mdl`

as 11 models, one for each regularization strength in `Lambda`

.

Estimate the test-sample edges.

e = edge(Mdl,X(:,testIdx),Ystats(testIdx),'ObservationsIn','columns')

`e = `*1×11*
0.9986 0.9986 0.9986 0.9986 0.9986 0.9932 0.9765 0.9176 0.8274 0.8128 0.8128

Because there are 11 regularization strengths, `e`

is a 1-by-11 vector of edges.

Plot the test-sample edges for each regularization strength. Identify the regularization strength that maximizes the edges over the grid.

figure; plot(log10(Lambda),log10(e),'-o') [~, maxEIdx] = max(e); maxLambda = Lambda(maxEIdx); hold on plot(log10(maxLambda),log10(e(maxEIdx)),'ro'); ylabel('log_{10} test-sample edge') xlabel('log_{10} Lambda') legend('Edge','Max edge') hold off

Several values of `Lambda`

yield similarly high edges. Higher values of lambda lead to predictor variable sparsity, which is a good quality of a classifier.

Choose the regularization strength that occurs just before the edge starts decreasing.

LambdaFinal = Lambda(5);

Train a linear classification model using the entire data set and specify the regularization strength yielding the maximal edge.

MdlFinal = fitclinear(X,Ystats,'ObservationsIn','columns',... 'Learner','logistic','Solver','sparsa','Regularization','lasso',... 'Lambda',LambdaFinal);

To estimate labels for new observations, pass `MdlFinal`

and the new data to `predict`

.

## More About

### Classification Edge

The *classification edge* is the weighted mean of the
classification margins.

One way to choose among multiple classifiers, for example to perform feature selection, is to choose the classifier that yields the greatest edge.

### Classification Margin

The *classification margin* for binary classification
is, for each observation, the difference between the classification score for the true class
and the classification score for the false class.

The software defines the classification margin for binary classification as

$$m=2yf\left(x\right).$$

*x* is an observation. If the true label of
*x* is the positive class, then *y* is 1, and –1
otherwise. *f*(*x*) is the positive-class classification
score for the observation *x*. The classification margin is commonly
defined as *m* =
*y**f*(*x*).

If the margins are on the same scale, then they serve as a classification confidence measure. Among multiple classifiers, those that yield greater margins are better.

### Classification Score

For linear classification models, the raw *classification
score* for classifying the observation *x*, a row vector,
into the positive class is defined by

$${f}_{j}(x)=x{\beta}_{j}+{b}_{j}.$$

For the model with regularization strength *j*, $${\beta}_{j}$$ is the estimated column vector of coefficients (the model property
`Beta(:,j)`

) and $${b}_{j}$$ is the estimated, scalar bias (the model property
`Bias(j)`

).

The raw classification score for classifying *x* into
the negative class is –*f*(*x*).
The software classifies observations into the class that yields the
positive score.

If the linear classification model consists of logistic regression learners, then the
software applies the `'logit'`

score transformation to the raw
classification scores (see `ScoreTransform`

).

## Algorithms

By default, observation weights are prior class probabilities. If you supply weights using
`Weights`

, then the software normalizes them to sum to the prior
probabilities in the respective classes. The software uses the normalized weights to
estimate the weighted edge.

## Extended Capabilities

### Tall Arrays

Calculate with arrays that have more rows than fit in memory.

Usage notes and limitations:

`edge`

does not support tall`table`

data.

For more information, see Tall Arrays.

## Version History

**Introduced in R2016a**

### R2022a: `edge`

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 `edge`

function returns a different value compared to previous releases.

The `edge`

function uses the prior
probabilities stored in the `Prior`

property to normalize the observation
weights of the input data. The way the function uses the `Prior`

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 can 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 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.

### R2022a: `edge`

can return NaN for predictor data with missing values

The `edge`

function no longer omits an observation with a
NaN score when computing the weighted mean of the classification margins. Therefore,
`edge`

can now return NaN when the predictor data
`X`

or the predictor variables in `Tbl`

contain any missing values. In most cases, if the test set observations do not contain
missing predictors, the `edge`

function does not return
NaN.

This change improves the automatic selection of a classification model when you use
`fitcauto`

.
Before this change, the software might select a model (expected to best classify new
data) with few non-NaN predictors.

If `edge`

in your code returns NaN, you can update your code
to avoid this result. Remove or replace the missing values by using `rmmissing`

or `fillmissing`

, respectively.

The following table shows the classification models for which the
`edge`

object function might return NaN. For more details,
see the Compatibility Considerations for each `edge`

function.

Model Type | Full or Compact Model Object | `edge` Object Function |
---|---|---|

Discriminant analysis classification model | `ClassificationDiscriminant` , `CompactClassificationDiscriminant` | `edge` |

Ensemble of learners for classification | `ClassificationEnsemble` , `CompactClassificationEnsemble` | `edge` |

Gaussian kernel classification model | `ClassificationKernel` | `edge` |

k-nearest neighbor classification model | `ClassificationKNN` | `edge` |

Linear classification model | `ClassificationLinear` | `edge` |

Neural network classification model | `ClassificationNeuralNetwork` , `CompactClassificationNeuralNetwork` | `edge` |

Support vector machine (SVM) classification model | `edge` |

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