# predict

**Class: **ClassificationLinear

Predict labels for linear classification models

## Syntax

## Description

`[`

also returns classification scores for both
classes using any of the input argument combinations in the previous syntaxes.
`Label`

,`Score`

]
= predict(___)`Score`

contains classification scores for each regularization strength
in `Mdl`

.

## 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 to be classified

full numeric matrix | sparse numeric matrix | table

Predictor data to be classified, specified as a full or sparse numeric matrix or a table.

By default, each row of `X`

corresponds to one observation, and
each column corresponds to one variable.

For a numeric matrix:

The variables in the columns of

`X`

must have the same order as the predictor variables that trained`Mdl`

.If you train

`Mdl`

using a table (for example,`Tbl`

) and`Tbl`

contains only numeric predictor variables, then`X`

can be a numeric matrix. To treat numeric predictors in`Tbl`

as categorical during training, identify categorical predictors by using the`CategoricalPredictors`

name-value pair argument of`fitclinear`

. If`Tbl`

contains heterogeneous predictor variables (for example, numeric and categorical data types) and`X`

is a numeric matrix, then`predict`

throws an error.

For a table:

`predict`

does not support multicolumn variables or cell arrays other than cell arrays of character vectors.If you train

`Mdl`

using a table (for example,`Tbl`

), then all predictor variables in`X`

must have the same variable names and data types as the variables that trained`Mdl`

(stored in`Mdl.PredictorNames`

). However, the column order of`X`

does not need to correspond to the column order of`Tbl`

. Also,`Tbl`

and`X`

can contain additional variables (response variables, observation weights, and so on), but`predict`

ignores them.If you train

`Mdl`

using a numeric matrix, then the predictor names in`Mdl.PredictorNames`

must be the same as the corresponding predictor variable names in`X`

. To specify predictor names during training, use the`PredictorNames`

name-value pair argument of`fitclinear`

. All predictor variables in`X`

must be numeric vectors.`X`

can contain additional variables (response variables, observation weights, and so on), but`predict`

ignores them.

**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 optimization execution time. You cannot specify
`'ObservationsIn','columns'`

for predictor data in a table.

**Data Types: **`table`

| `double`

| `single`

`dimension`

— Predictor data observation dimension

`'rows'`

(default) | `'columns'`

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

or
`'rows'`

.

**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 optimization execution time. You cannot specify
`'ObservationsIn','columns'`

for predictor data in a table.

## Output Arguments

`Label`

— Predicted class labels

categorical array | character array | logical matrix | numeric matrix | cell array of character vectors

Predicted class labels, returned as a categorical or character array, logical or numeric matrix, or cell array of character vectors.

In most cases, `Label`

is an *n*-by-*L*
array of the same data type as the observed class labels (`Y`

) used to
train `Mdl`

. (The software treats string arrays as cell arrays of character
vectors.)
*n* is the number of observations in `X`

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

. That is,
`Label(`

is the predicted class label for observation * i*,

*)*

`j`

*using the linear classification model that has regularization strength*

`i`

`Mdl.Lambda(``j`

)

.If `Y`

is a character array and *L* >
1, then `Label`

is a cell array of class labels.

`Score`

— Classification scores

numeric array

Classification
scores, returned as a *n*-by-2-by-*L* numeric
array. *n* is the number of observations in `X`

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

. `Score(`

is
the score for classifying observation * i*,

*,*

`k`

*)*

`j`

*into class*

`i`

*using the linear classification model that has regularization strength*

`k`

`Mdl.Lambda(``j`

)

. `Mdl.ClassNames`

stores
the order of the classes.If `Mdl.Learner`

is `'logistic'`

,
then classification scores are posterior probabilities.

## Examples

### Predict Training-Sample Labels

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 using the entire data set, which can identify whether the word counts in a documentation web page are from the Statistics and Machine Learning Toolbox™ documentation.

```
rng(1); % For reproducibility
Mdl = fitclinear(X,Ystats);
```

`Mdl`

is a `ClassificationLinear`

model.

Predict the training-sample, or resubstitution, labels.

label = predict(Mdl,X);

Because there is one regularization strength in `Mdl`

, `label`

is column vectors with lengths equal to the number of observations.

Construct a confusion matrix.

ConfusionTrain = confusionchart(Ystats,label);

The model misclassifies only one `'stats'`

documentation page as being outside of the Statistics and Machine Learning Toolbox documentation.

### Predict Test-Sample Labels

Load the NLP data set and preprocess it as in Predict Training-Sample Labels. Transpose the predictor data matrix.

load nlpdata Ystats = Y == 'stats'; X = X';

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,... 'ObservationsIn','columns'); Mdl = 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);

Predict the training- and test-sample labels.

labelTrain = predict(Mdl,X(:,trainIdx),'ObservationsIn','columns'); labelTest = predict(Mdl,X(:,testIdx),'ObservationsIn','columns');

Because there is one regularization strength in `Mdl`

, `labelTrain`

and `labelTest`

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

Construct a confusion matrix for the training data.

ConfusionTrain = confusionchart(Ystats(trainIdx),labelTrain);

The model misclassifies only three documentation pages as being outside of Statistics and Machine Learning Toolbox documentation.

Construct a confusion matrix for the test data.

ConfusionTest = confusionchart(Ystats(testIdx),labelTest);

The model misclassifies three documentation pages as being outside the Statistics and Machine Learning Toolbox, and two pages as being inside.

### Estimate Posterior Class Probabilities

Estimate test-sample, posterior class probabilities, and determine the quality of the model by plotting a receiver operating characteristic (ROC) curve. Linear classification models return posterior probabilities for logistic regression learners only.

Load the NLP data set and preprocess it as in Predict Test-Sample Labels.

load nlpdata Ystats = Y == 'stats'; X = X';

Randomly partition the data into training and test sets by specifying a 30% holdout sample. Identify the test-set indices.

```
cvp = cvpartition(Ystats,'Holdout',0.30);
idxTest = test(cvp);
```

Train a binary linear classification model. Fit logistic regression learners using SpaRSA. To hold out the test set, specify the partitioned model.

CVMdl = fitclinear(X,Ystats,'ObservationsIn','columns','CVPartition',cvp,... 'Learner','logistic','Solver','sparsa'); Mdl = CVMdl.Trained{1};

`Mdl`

is a `ClassificationLinear`

model trained using the training set specified in the partition `cvp`

only.

Predict the test-sample posterior class probabilities.

[~,posterior] = predict(Mdl,X(:,idxTest),'ObservationsIn','columns');

Because there is one regularization strength in `Mdl`

, `posterior`

is a matrix with 2 columns and rows equal to the number of test-set observations. Column *i* contains posterior probabilities of `Mdl.ClassNames(i)`

given a particular observation.

Compute the performance metrics (true positive rates and false positive rates) for a ROC curve and find the area under the ROC curve (AUC) value by creating a `rocmetrics`

object.

rocObj = rocmetrics(Ystats(idxTest),posterior,Mdl.ClassNames);

Plot the ROC curve for the second class by using the `plot`

function of `rocmetrics`

.

plot(rocObj,ClassNames=Mdl.ClassNames(2))

The ROC curve indicates that the model classifies the test-sample observations almost perfectly.

### Find Good Lasso Penalty Using AUC

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

Load the NLP data set. Preprocess the data as in Predict Test-Sample Labels.

load nlpdata Ystats = Y == 'stats'; X = X';

Create a data partition that specifies to holdout 10% of the observations. Extract test-sample indices.

rng(10); % For reproducibility Partition = cvpartition(Ystats,'Holdout',0.10); testIdx = test(Partition); XTest = X(:,testIdx); n = sum(testIdx)

n = 3157

YTest = Ystats(testIdx);

There are 3157 observations in the test sample.

Create a set of 11 logarithmically-spaced regularization strengths from $$1{0}^{-6}$$ through $$1{0}^{-0.5}$$.

Lambda = logspace(-6,-0.5,11);

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`

.

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.

Mdl1 = CVMdl.Trained{1}

Mdl1 = ClassificationLinear ResponseName: 'Y' ClassNames: [0 1] ScoreTransform: 'logit' Beta: [34023x11 double] Bias: [-11.9117 -11.9117 -11.9117 -11.9117 -9.5080 -6.3565 ... ] Lambda: [1.0000e-06 3.5481e-06 1.2589e-05 4.4668e-05 ... ] 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 predicted labels and posterior class probabilities.

[label,posterior] = predict(Mdl1,XTest,'ObservationsIn','columns'); Mdl1.ClassNames; posterior(3,1,5)

ans = 1.0000

`label`

is a 3157-by-11 matrix of predicted labels. Each column corresponds to the predicted labels of the model trained using the corresponding regularization strength. `posterior`

is a 3157-by-2-by-11 matrix of posterior class probabilities. Columns correspond to classes and pages correspond to regularization strengths. For example, `posterior(3,1,5)`

indicates that the posterior probability that the first class (label `0`

) is assigned to observation 3 by the model that uses `Lambda(5)`

as a regularization strength is 1.0000.

For each model, compute the AUC by using `rocmetrics`

.

auc = 1:numel(Lambda); % Preallocation for j = 1:numel(Lambda) rocObj = rocmetrics(YTest,posterior(:,:,j),Mdl1.ClassNames); auc(j) = rocObj.AUC(1); end

Higher values of `Lambda`

lead to predictor variable sparsity, which is a good quality of a classifier. For each regularization strength, train a linear classification model using the entire data set and the same options as when you trained the model. Determine the number of nonzero coefficients per model.

Mdl = fitclinear(X,Ystats,'ObservationsIn','columns',... 'Learner','logistic','Solver','sparsa','Regularization','lasso',... 'Lambda',Lambda,'GradientTolerance',1e-8); numNZCoeff = sum(Mdl.Beta~=0);

In the same figure, plot the test-sample error rates and frequency of nonzero coefficients for each regularization strength. Plot all variables on the log scale.

figure yyaxis left plot(log10(Lambda),log10(auc),'o-') ylabel('log_{10} AUC') yyaxis right plot(log10(Lambda),log10(numNZCoeff + 1),'o-') ylabel('log_{10} nonzero-coefficient frequency') xlabel('log_{10} Lambda') title('Test-Sample Statistics') hold off

Choose the index of the regularization strength that balances predictor variable sparsity and high AUC. In this case, a value between $$1{0}^{-2}$$ to $$1{0}^{-1}$$ should suffice.

idxFinal = 9;

Select the model from `Mdl`

with the chosen regularization strength.

MdlFinal = selectModels(Mdl,idxFinal);

`MdlFinal`

is a `ClassificationLinear`

model containing one regularization strength. To estimate labels for new observations, pass `MdlFinal`

and the new data to `predict`

.

## More About

### 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`

).

## Extended Capabilities

### Tall Arrays

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

Usage notes and limitations:

`predict`

does not support tall`table`

data.

For more information, see Tall Arrays.

### C/C++ Code Generation

Generate C and C++ code using MATLAB® Coder™.

Usage notes and limitations:

You can generate C/C++ code for both

`predict`

and`update`

by using a coder configurer. Or, generate code only for`predict`

by using`saveLearnerForCoder`

,`loadLearnerForCoder`

, and`codegen`

.Code generation for

`predict`

and`update`

— Create a coder configurer by using`learnerCoderConfigurer`

and then generate code by using`generateCode`

. Then you can update model parameters in the generated code without having to regenerate the code.Code generation for

`predict`

— Save a trained model by using`saveLearnerForCoder`

. Define an entry-point function that loads the saved model by using`loadLearnerForCoder`

and calls the`predict`

function. Then use`codegen`

(MATLAB Coder) to generate code for the entry-point function.

To generate single-precision C/C++ code for

`predict`

, specify the name-value argument`"DataType","single"`

when you call the`loadLearnerForCoder`

function.This table contains notes about the arguments of

`predict`

. Arguments not included in this table are fully supported.Argument Notes and Limitations `Mdl`

For the usage notes and limitations of the model object, see Code Generation of the

`ClassificationLinear`

object.`X`

For general code generation,

`X`

must be a single-precision or double-precision matrix or a table containing numeric variables, categorical variables, or both.In the coder configurer workflow,

`X`

must be a single-precision or double-precision matrix.The number of observations in

`X`

can be a variable size, but the number of variables in`X`

must be fixed.If you want to specify

`X`

as a table, then your model must be trained using a table, and your entry-point function for prediction must do the following:Accept data as arrays.

Create a table from the data input arguments and specify the variable names in the table.

Pass the table to

`predict`

.

For an example of this table workflow, see Generate Code to Classify Data in Table. For more information on using tables in code generation, see Code Generation for Tables (MATLAB Coder) and Table Limitations for Code Generation (MATLAB Coder).

Name-value pair arguments Names in name-value pair arguments must be compile-time constants.

The value for the

`'ObservationsIn'`

name-value pair argument must be a compile-time constant. For example, to use the`'ObservationsIn','columns'`

name-value pair argument in the generated code, include`{coder.Constant('ObservationsIn'),coder.Constant('columns')}`

in the`-args`

value of`codegen`

(MATLAB Coder).

For more information, see Introduction to Code Generation.

## Version History

**Introduced in R2016a**

## See Also

`ClassificationLinear`

| `loss`

| `fitclinear`

| `confusionchart`

| `rocmetrics`

| `testcholdout`

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