In the MATLAB^® Command Window, load the humanactivity data set, and create a table from the variables in the data set to use for classification. The data set contains 24,075 observations of five physical human activities: sitting, standing, walking, running, and dancing. Each observation has 60 features extracted from acceleration data measured by smartphone accelerometer sensors.

load humanactivity
Tbl = array2table(feat);
Tbl.Properties.VariableNames = featlabels';
activity = categorical(actid,1:5,actnames);
Tbl.Activity = activity;

Alternatively, you can load the humanactivity data set, create the categorical activity response variable, and keep the feat and activity data as separate variables.

Click the Apps tab, and then click the Show more arrow on the right to open the apps gallery. In the Machine Learning and Deep Learning group, click Classification Learner.

On the Learn tab, in the File section, click New Session and select From Workspace.

In the New Session from Workspace dialog box, select the table Tbl from the Data Set Variable list. Note that the app selects response and predictor variables based on their data types. In particular, the app selects Activity as the response variable because it is the only categorical variable. For this example, do not change the selections.

Alternatively, if you keep the predictor data feat and response variable activity as two separate variables, you can first select the matrix feat from the Data Set Variable list. Then, under Response, click the From workspace option button and select activity from the list.

To accept the default validation scheme and continue, click Start Session. The default validation option is 5-fold cross-validation, to protect against overfitting.

Classification Learner creates a scatter plot of the data.

Use the scatter plot to investigate which variables are useful for predicting the response. Select different options in the X and Y lists under Predictors to visualize the distribution of activities and measurements. Note which variables separate the activities (colors) most clearly.

Create a selection of kernel approximation models. On the Learn tab, in the Models section, click the arrow to open the gallery. In the Kernel Approximation Classifiers group, click All Kernels.

In the Train section, click Train All and select Train All.

Classification Learner trains one of each kernel approximation option in the gallery, as well as the default fine tree model. In the Models pane, the app outlines the Accuracy (Validation) score of the best model. Classification Learner also displays a validation confusion matrix for the first kernel model (SVM Kernel).

To view the results for a model, double-click the model in the Models pane, and inspect the model Summary tab. The Summary tab displays the Training Results and Additional Training Results metrics, calculated on the validation set.

Select the second kernel model (Logistic Regression Kernel) in the Models pane, and inspect the accuracy of the predictions in each class using a validation confusion matrix. On the Learn tab, in the Plots and Results section, click the arrow to open the gallery, and then click Confusion Matrix (Validation) in the Validation Results group. View the matrix of true class and predicted class results.

Compare the confusion matrices for the two kernel models side-by-side. First, close the plot and summary tabs for Model 1. On the Learn tab, in the Plots and Results section, click the Layout button and select Compare models. In the top right of each plot, click the Hide plot options button to make more room for the plot.

To return to the original layout, you can click the Layout button and select Single model (Default).

Choose the best kernel model in the Models pane (the best overall score is highlighted in the Accuracy (Validation) box). See if you can improve the model by removing features with low predictive power.

First, duplicate the best kernel model by right-clicking the model and selecting Duplicate.

Investigate features to include or exclude using one of these methods.

Train the model. On the Learn tab, in the Train section, click Train All and select Train Selected to train the model using the new options. Compare results among the classifiers in the Models pane.

Choose the best kernel model in the Models pane. To try to improve the model further, change its hyperparameters. First, duplicate the model by right-clicking the model and selecting Duplicate. Then, try changing some hyperparameter settings in the model Summary tab. Train the new model by clicking Train All and selecting Train Selected in the Train section.

To learn more about kernel model settings, see Kernel Approximation Classifiers.

You can export a compact version of the trained model to the workspace. In the Export section of the Learn tab, click Export Model and select Export Model. In the Export Classification Model dialog box, the check box to include the training data is disabled because kernel approximation models do not store training data. In the dialog box, click OK to accept the default variable name.

To examine the code for training this classifier, on the Learn tab, in the Export section, click Export Model and select Generate Function. Because the data set used to train this classifier has more than two classes, the generated code uses the fitcecoc function rather than fitckernel.

On the Learn tab, in the Models section, click the arrow to open the gallery of classification models.

In the Get Started group, click All.

In the Train section, click Train All and select Train All.