Note: This page has been translated by MathWorks. Click here to see

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

To view all translated materials including this page, select Country from the country navigator on the bottom of this page.

Classification Learner is well suited for choosing and training
classification models interactively, but it does not generate C/C++ code that labels data
based on a trained model. The **Generate Function** button in the
**Export** section of the Classification Learner app generates MATLAB
code for training a model but does not generate C/C++ code. This example shows how to
generate C code from a function that predicts labels using an exported classification model.
The example builds a model that predicts the credit rating of a business given various
financial ratios, according to these steps:

Use the credit rating data set in the file

`CreditRating_Historical.dat`

, which is included with Statistics and Machine Learning Toolbox™.Reduce the data dimensionality using principal component analysis (PCA).

Train a set of models that support code generation for label prediction.

Export the model with the minimum 5-fold, cross-validated classification accuracy.

Generate C code from an entry-point function that transforms the new predictor data and then predicts corresponding labels using the exported model.

Load sample data and import the data into the Classification Learner app. Review the data using scatter plots and remove unnecessary predictors.

Use `readtable`

to load the historical credit rating data set in
the file `CreditRating_Historical.dat`

into a table.

```
creditrating = readtable('CreditRating_Historical.dat');
```

On the **Apps** tab, click **Classification
Learner**.

In Classification Learner, on the **Classification Learner** tab,
in the **File** section, click **New Session** and
select **From Workspace**.

In the New Session dialog box, select the table `creditrating`

. All
variables, except the one identified as the response, are double-precision numeric
vectors. Click **Start Session** to compare classification models
based on the 5-fold, cross-validated classification accuracy.

Classification Learner loads the data and plots a scatter plot of the variables
`WC_TA`

versus `ID`

. Because identification numbers
are not helpful to display in a plot, choose `RE_TA`

for
**X** under **Predictors**.

The scatter plot suggests that the two variables can separate the classes
`AAA`

, `BBB`

, `BB`

, and
`CCC`

fairly well. However, the observations corresponding to the
remaining classes are mixed into these classes.

Identification numbers are not helpful for prediction. Therefore, in the
**Features** section, click **Feature Selection**
and then clear the **ID** check box. You can also remove
unnecessary predictors from the beginning by using the check boxes in the New Session
dialog box. This example shows how to remove unused predictors for code generation when
you have included all predictors.

Enable PCA to reduce the data dimensionality.

In the **Features** section, click **PCA**,
and then select **Enable PCA**. This action applies PCA to the
predictor data, and then transforms the data before training the models. Classification
Learner uses only components that collectively explain 95% of the variability.

Train a set of models that support code generation for label prediction.

Select the following classification models and options, which support code
generation for label prediction, and then perform cross-validation (for more details,
see Code Generation Support, Usage Notes, and Limitations). To select each model,
in the **Model Type** section, click the **Show more**
arrow, and then click the model. After selecting a model and specifying any options,
close any open menus, and then click **Train** in the
**Training** section.

Models and Options to Select | Description |
---|---|

Under Decision Trees, select All
Trees | Classification trees of various complexities |

Under Support Vector Machines, select
All SVMs | SVMs of various complexities and using various kernels. Complex SVMs require time to fit. |

Under Ensemble Classifiers, select
Boosted Trees. In the Model
Type section, click Advanced. Reduce
Maximum number of splits to 5
and increase Number of learners to
100. | Boosted ensemble of classification trees |

Under Ensemble Classifiers, select
Bagged Trees. In the Model
Type section, click Advanced. Increase
Maximum number of splits to 50
and increase Number of learners to
100. | Random forest of classification trees |

After cross-validating each model type, the Data Browser displays each model and its 5-fold, cross-validated classification accuracy, and highlights the model with the best accuracy.

Select the model that yields the maximum 5-fold, cross-validated classification accuracy, which is the error-correcting output codes (ECOC) model of Fine Gaussian SVM learners. With PCA enabled, Classification Learner uses two predictors out of six.

In the **Plots** section, click **Confusion
Matrix**.

The model does well distinguishing between `A`

,
`B`

, and `C`

classes. However, the model does not do
as well distinguishing between particular levels within those groups, the lower B levels
in particular.

Export the model to the MATLAB^{®} Workspace and save the model using `saveCompactModel`

.

In the **Export** section, click **Export
Model**, and then select **Export Compact Model**.
Click **OK** in the dialog box.

The structure `trainedModel`

appears in the MATLAB Workspace. The field `ClassificationSVM`

of
`trainedModel`

contains the compact model.

At the command line, save the compact model to a file called
`ClassificationLearnerModel.mat`

in your current folder.

`saveCompactModel(trainedModel.ClassificationSVM,'ClassificationLearnerModel')`

Prediction using the object functions requires a trained model object, but the
`-args`

option of `codegen`

does not accept such objects. Work around this limitation by using
`saveCompactModel`

and `loadCompactModel`

. Save a trained model by using
`saveCompactModel`

. Then, define an entry-point function that loads the
saved model by using `loadCompactModel`

and calls the
`predict`

function. Finally, use `codegen`

to
generate code for the entry-point function.

Preprocess new data in the same way you preprocess the training data.

To preprocess, you need the following three model parameters:

`removeVars`

— Column vector of at most`p`

elements identifying indices of variables to remove from the data, where`p`

is the number of predictor variables in the raw data`pcaCenters`

— Row vector of exactly`q`

PCA centers`pcaCoefficients`

—`q`

-by-`r`

matrix of PCA coefficients, where`r`

is at most`q`

Specify the indices of predictor variables that you removed while selecting data
using **Feature Selection** in Classification Learner. Extract the
PCA statistics from `trainedModel`

.

removeVars = 1; pcaCenters = trainedModel.PCACenters; pcaCoefficients = trainedModel.PCACoefficients;

Save the model parameters to a file named `ModelParameters.mat`

in your current folder.

save('ModelParameters.mat','removeVars','pcaCenters','pcaCoefficients');

An entry-point function is a function you define for code
generation. Because you cannot call any function at the top level using
`codegen`

, you must define an entry-point function that calls
code-generation-enabled functions, and then generate C/C++ code for the entry-point
function by using codegen.

In your current folder, define a function named `mypredictCL.m`

that:

Accepts a numeric matrix (

`X`

) of raw observations containing the same predictor variables as the ones passed into Classification LearnerLoads the classification model in

`ClassificationLearnerModel.mat`

and the model parameters in`ModelParameters.mat`

Removes the predictor variables corresponding to the indices in

`removeVars`

Transforms the remaining predictor data using the PCA centers (

`pcaCenters`

) and coefficients (`pcaCoefficients`

) estimated by Classification LearnerReturns predicted labels using the model

function label = mypredictCL(X) %#codegen %MYPREDICTCL Classify credit rating using model exported from %Classification Learner % MYPREDICTCL loads trained classification model (SVM) and model % parameters (removeVars, pcaCenters, and pcaCoefficients), removes the % columns of the raw matrix of predictor data in X corresponding to the % indices in removeVars, transforms the resulting matrix using the PCA % centers in pcaCenters and PCA coefficients in pcaCoefficients, and then % uses the transformed data to classify credit ratings. X is a numeric % matrix with n rows and 7 columns. label is an n-by-1 cell array of % predicted labels. % Load trained classification model and model parameters SVM = loadCompactModel('ClassificationLearnerModel'); data = coder.load('ModelParameters'); removeVars = data.removeVars; pcaCenters = data.pcaCenters; pcaCoefficients = data.pcaCoefficients; % Remove unused predictor variables keepvars = 1:size(X,2); idx = ~ismember(keepvars,removeVars); keepvars = keepvars(idx); XwoID = X(:,keepvars); % Transform predictors via PCA Xpca = bsxfun(@minus,XwoID,pcaCenters)*pcaCoefficients; % Generate label from SVM label = predict(SVM,Xpca); end

Because C and C++ are statically typed languages, you must
determine the properties of all variables in the entry-point function at compile time.
Specify variable-size arguments using `coder.typeof`

and generate code using the arguments.

Create a double-precision matrix called `x`

for code generation
using `coder.typeof`

. Specify that the number of
rows of `x`

is arbitrary, but that `x`

must have
`p`

columns.

p = size(creditrating,2) - 1; x = coder.typeof(0,[Inf,p],[1 0]);

For more details about specifying variable-size arguments, see Specify Variable-Size Arguments for Code Generation.

Generate a MEX function from `mypredictCL.m`

. Use the
`-args`

option to specify `x`

as an
argument.

codegen mypredictCL -args x

`codegen`

generates the MEX file
`mypredictCL_mex.mexw64`

in your current folder. The file extension
depends on your platform.

Verify that the MEX function returns the expected labels.

Remove the response variable from the original data set, and then randomly draw 15 observations.

rng('default'); % For reproducibility m = 15; testsampleT = datasample(creditrating(:,1:(end - 1)),m);

Predict corresponding labels by using `predictFcn`

in the
classification model trained by Classification Learner.

testLabels = trainedModel.predictFcn(testsampleT);

Convert the resulting table to a matrix.

testsample = table2array(testsampleT);

The columns of `testsample`

correspond to the columns of the
predictor data loaded by Classification Learner.

Pass the test data to `mypredictCL`

. The function
`mypredictCL`

predicts corresponding labels by using
`predict`

and the classification model trained by Classification
Learner.

testLabelsPredict = mypredictCL(testsample);

Predict corresponding labels by using the generated MEX function
`mypredictCL_mex`

.

testLabelsMEX = mypredictCL_mex(testsample);

Compare the sets of predictions.

isequal(testLabels,testLabelsMEX,testLabelsPredict)

ans = logical 1

`isequal`

returns logical 1 (true) if all
the inputs are equal. `predictFcn`

, `mypredictCL`

,
and the MEX function return the same values.

`codegen`

| `coder.typeof`

| `learnerCoderConfigurer`

| `loadCompactModel`

| `saveCompactModel`

- Classification Learner App
- Introduction to Code Generation
- Code Generation for Prediction of Machine Learning Model at Command Line
- Code Generation for Prediction of Machine Learning Model Using MATLAB Coder App
- Code Generation for Prediction and Update Using Coder Configurer
- Specify Variable-Size Arguments for Code Generation
- Apply PCA to New Data and Generate C/C++ Code