**Class: **ClassificationLinear

Classification margins for linear classification models

uses
any of the previous syntaxes and additional options specified by one
or more `m`

= margin(___,`Name,Value`

)`Name,Value`

pair arguments. For example,
you can specify that columns in the predictor data correspond to observations.

`Mdl`

— Binary, linear classification model`ClassificationLinear`

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

model object.
You can create a `ClassificationLinear`

model object
using `fitclinear`

.

`X`

— Predictor datafull 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.

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 labelscategorical 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`

and the number of observations in`X`

must be equal.

**Data Types: **`categorical`

| `char`

| `string`

| `logical`

| `single`

| `double`

| `cell`

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

`'ObservationsIn'`

— Predictor data observation dimension`'rows'`

(default) | `'columns'`

Predictor data observation dimension, specified as the comma-separated
pair consisting of `'ObservationsIn'`

and `'columns'`

or `'rows'`

.

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

,
then you might experience a significant reduction in optimization-execution
time.

`m`

— Classification marginsnumeric column vector | numeric matrix

Classification margins, returned as a numeric column vector or matrix.

`m`

is *n*-by-*L*,
where *n* is the number of observations in `X`

and *L* is
the number of regularization strengths in `Mdl`

(that
is, `numel(Mdl.Lambda)`

).

`m(`

is
the classification margin of observation * i*,

`j`

`Mdl.Lambda(``j`

)

.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 hold out 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 margins.

mTrain = margin(CMdl,X(trainIdx,:),Ystats(trainIdx)); mTest = margin(CMdl,X(testIdx,:),Ystats(testIdx));

Because there is one regularization strength in `CMdl`

, `mTrain`

and `mTest`

are column vectors with lengths equal to the number of training and test observations, respectively.

Plot both sets of margins using box plots.

figure; boxplot([mTrain; mTest],[zeros(size(mTrain,1),1); ones(size(mTest,1),1)], ... 'Labels',{'Training set','Test set'}); h = gca; h.YLim = [-5 60]; title 'Training- and Test-Set Margins'

The distributions of the margins between the training and test sets appear similar.

One way to perform feature selection is to compare test-sample margins from multiple models. Based solely on this criterion, the classifier with the larger margins is the better classifier.

Load the NLP data set. Preprocess the data as in Estimate Test-Sample Margins.

load nlpdata 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 10% of the predictor variables.

```
p = size(X,1); % Number of predictors
idxPart = randsample(p,ceil(0.1*p));
```

Train two binary, linear classification models: one that uses the all of the predictors and one that uses the random 10%. 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 margins for each classifier. Plot the distribution of the margins sets using box plots.

fullMargins = margin(CMdl,XTest,YTest,'ObservationsIn','columns'); partMargins = margin(PCMdl,XTest(idxPart,:),YTest,... 'ObservationsIn','columns'); figure; boxplot([fullMargins partMargins],'Labels',... {'All Predictors','10% of the Predictors'}); h = gca; h.YLim = [-20 60]; title('Test-Sample Margins')

The margin distribution of `CMdl`

is situated higher than the margin distribution of `PCMdl`

.

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

Load the NLP data set. Preprocess the data as in Estimate Test-Sample Margins.

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 = classreg.learning.partition.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: [1x11 double] Lambda: [1x11 double] 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 margins.

m = margin(Mdl,X(:,testIdx),Ystats(testIdx),'ObservationsIn','columns'); size(m)

`ans = `*1×2*
9471 11

Because there are 11 regularization strengths, `m`

has 11 columns.

Plot the test-sample margins for each regularization strength. Because logistic regression scores are in [0,1], margins are in [-1,1]. Rescale the margins to help identify the regularization strength that maximizes the margins over the grid.

figure; boxplot(10000.^m) ylabel('Exponentiated test-sample margins') xlabel('Lambda indices')

Several values of `Lambda`

yield margin distributions that are compacted near $$1000{0}^{1}$$. 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 centers of the margin distributions start decreasing.

LambdaFinal = Lambda(5);

Train a linear classification model using the entire data set and specify the desired regularization strength.

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`

.

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.

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`

).

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

This function fully supports tall arrays. For more information, see Tall Arrays (MATLAB).

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