Predict Class Labels Using ClassificationTree Predict Block

This example shows how to train a classification decision tree model using the Classification Learner app, and then use the ClassificationTree Predict block for label prediction in Simulink®. The block accepts an observation (predictor data) and returns the predicted class label and class score for the observation using the trained classification decision tree model.

Train Classification Model in Classification Learner App

Train a classification decision tree model by using hyperparameter optimization in the Classification Learner App.

1. In the MATLAB® Command Window, load the ionosphere data set, which contains radar return qualities (Y) and predictor data (X) of 34 variables. Radar returns are either of good quality ('g') or bad quality ('b').

Load the ionosphere data set. Determine the sample size.

load ionosphere
n = numel(Y)

n = 
351

Suppose that the radar returns are detected in sequence, and you have the first 300 observations, but you have not received the last 51 yet. Partition the data into present and future samples.

prsntX = X(1:300,:);
prsntY = Y(1:300);
ftrX = X(301:end,:);
ftrY = Y(301:end);

2. Open Classification Learner. On the Apps tab, in the Apps section, click the Show more arrow to display the apps gallery. In the Machine Learning and Deep Learning group, click Classification Learner.

3. On the Classification Learner tab, in the File section, click New Session and select From Workspace.

4. In the New Session from Workspace dialog box, select the matrix prsntX from the Data Set Variable list. Under Response, click the From workspace option button and select the vector prsntY from the workspace. The default validation option is 5-fold cross-validation, to protect against overfitting. For this example, do not change the default settings.

5. To accept the default options and continue, click Start Session.

6. Select an optimizable tree model to train. On the Learn tab, in the Models section, click the Show more arrow to open the gallery. In the Decision Trees group, click Optimizable Tree.

7. In the Train section, click Train All and select Train Selected. The app displays a Minimum Classification Error Plot as it runs the optimization process. At each iteration, the app tries a different combination of hyperparameter values and updates the plot with the minimum validation classification error observed up to that iteration, indicated in dark blue. When the app completes the optimization process, it selects the set of optimized hyperparameters, indicated by a red square. For more information, see Minimum Classification Error Plot.

The app lists the optimized hyperparameters in both the Optimization Results section to the right of the plot and the Model Hyperparameters section of the model Summary tab. In general, the optimization results are not reproducible.

8. Export the model to the MATLAB workspace. On the Classification Learner tab, in the Export section, click Export Model and select Export Model, then click OK. The default name for the exported model is trainedModel.

Alternatively, you can generate MATLAB code that trains a classification model with the same settings used to train the model in the app. On the Classification Learner tab, in the Export section, click Generate Function. The app generates code from your session and displays the file in the MATLAB Editor. The file defines a function that accepts predictor and response variables, trains a classification model, and performs cross-validation. Change the function name to trainClassificationTreeModel and save the function file. Train a decision tree classification model by using the trainClassificationTreeModel function.

trainedModel = trainClassificationTreeModel(prsntX,prsntY);

9. Extract the trained model from the trainedModel variable. trainedModel contains a ClassificationTree model object in the ClassificationTree field.

treeMdl = trainedModel.ClassificationTree;

Because hyperparameter optimization can lead to an overfitted model, the recommended approach is to create a separate test set before importing your data into the Classification Learner app and see how the optimized model performs on your test set. For more details, see Train Classifier Using Hyperparameter Optimization in Classification Learner App.

Create Simulink Model

This example provides the Simulink model slexIonosphereClassificationTreePredictExample.slx, which includes the ClassificationTree Predict block. You can open the Simulink model or create a new model as described in this section.

Open the Simulink model slexIonosphereClassificationTreePredictExample.slx.

SimMdlName = 'slexIonosphereClassificationTreePredictExample'; 
open_system(SimMdlName)

If you open the Simulink model, then the software runs the code in the PreLoadFcn callback function before loading the Simulink model. The PreLoadFcn callback function of slexIonosphereClassificationTreePredictExample includes code to check if your workspace contains the treeMdl variable for the trained model. If the workspace does not contain the variable, PreLoadFcn loads the sample data, trains the tree model, and creates an input signal for the Simulink model. To view the callback function, in the Setup section on the Modeling tab, click Model Settings and select Model Properties. Then, on the Callbacks tab, select the PreLoadFcn callback function in the Model callbacks pane.

To create a new Simulink model, open the Blank Model template and add the ClassificationTree Predict block. Add the Inport and Outport blocks and connect them to the ClassificationTree Predict block.

Double-click the ClassificationTree Predict block to open the Block Parameters dialog box. You can specify the name of a workspace variable that contains the trained model. The default variable name is treeMdl. Click the Refresh button. The dialog box displays the options used to train the model treeMdl under Trained Machine Learning Model. Select the Add output port for predicted class scores check box to add the second output port score.

The ClassificationTree Predict block expects an observation containing 34 predictor values. Double-click the Inport block, and set the Port dimensions to 34 on the Signal Attributes tab.

Create an input signal in the form of a structure array for the Simulink model. The structure array must contain these fields:

• time — The points in time at which the observations enter the model. In this example, the duration includes the integers from 0 through 50. The orientation must correspond to the observations in the predictor data. So, in this case, time must be a column vector.

• signals — A 1-by-1 structure array describing the input data and containing the fields values and dimensions, where values is a matrix of predictor data, and dimensions is the number of predictor variables.

Create an appropriate structure array for future radar returns.

radarReturnInput.time = (0:50)';
radarReturnInput.signals(1).values = ftrX;
radarReturnInput.signals(1).dimensions = size(ftrX,2);

To import signal data from the workspace:

• Open the Configuration Parameters dialog box. On the Modeling tab, click Model Settings.

• In the Data Import/Export pane, select the Input check box and enter radarReturnInput in the adjacent text box.

• In the Solver pane, under Simulation time, set Stop time to radarReturnInput.time(end). Under Solver selection, set Type to Fixed-step, and set Solver to discrete (no continuous states).

For more details, see Load Signal Data for Simulation (Simulink).

Simulate the model.

sim(SimMdlName);

When the Inport block detects an observation, it directs the observation into the ClassificationTree Predict block. You can use the Simulation Data Inspector (Simulink) to view the logged data of the Outport blocks.