In the MATLAB^® Command Window, load the Fisher iris data set and create a table of measurement predictors (or features) using variables from the data set.

fishertable = readtable("fisheriris.csv");

Click the Apps tab, and then click the arrow at the right of the Apps section to open the apps gallery. In the Machine Learning and Deep Learning group, click Classification Learner.

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

In the New Session from Workspace dialog box, select the table fishertable from the Data Set Variable list (if necessary).

As shown in the dialog box, the app selects the response and predictor variables based on their data type. Petal and sepal length and width are predictors, and species is the response that you want to classify. For this example, do not change the selections.

To accept the default validation scheme and continue, click Start Session. The default validation option is 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 on the X and Y lists under Predictors to visualize the distribution of species and measurements. Observe which variables separate the species colors most clearly.

The setosa species (blue points) is easy to separate from the other two species with all four predictors. The versicolor and virginica species are much closer together in all predictor measurements and overlap, especially when you plot sepal length and width. setosa is easier to predict than the other two species.

Create a naive Bayes model. On the Learn tab, in the Models section, click the arrow to open the gallery. In the Naive Bayes Classifiers group, click Gaussian Naive Bayes. Note that the Model Hyperparameters section of the model Summary tab contains no hyperparameter options.

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

The app creates a Gaussian naive Bayes model, and plots a validation confusion matrix.

The app displays the Gaussian Naive Bayes model in the Models pane. Check the model validation accuracy in the Accuracy (Validation) box. The value shows that the model performs well.

For the Gaussian Naive Bayes model, by default, the app models the distribution of numerical predictors using the Gaussian distribution, and models the distribution of categorical predictors using the multivariate multinomial distribution (MVMN).

Examine the scatter plot for the trained model. On the Learn tab, in the Plots and Results section, click the arrow to open the gallery, and then click Scatter in the Validation Results group. An X indicates a misclassified point. The blue points (setosa species) are all correctly classified, but the other two species have misclassified points. Under Plot, switch between the Data and Model predictions options. Observe the color of the incorrect (X) points. Or, to view only the incorrect points, clear the Correct check box.

Train a kernel naive Bayes model for comparison. On the Learn tab, in the Models gallery, click Kernel Naive Bayes. The app displays a draft kernel naive Bayes model in the Models pane.

In the model Summary tab, under Model Hyperparameters, select Triangle from the Kernel type list, select Positive from the Support list, and select No from the Standardize data list.

In the Train section, click Train All and select Train Selected to train the new model.

The Models pane displays the model validation accuracy for the new kernel naive Bayes model. Its validation accuracy is better than the validation accuracy of the Gaussian naive Bayes model. The app highlights the Accuracy (Validation) value of the best model (or models) by outlining it in a box.

In the Models pane, click each model to view and compare the results. To view the results for a model, inspect the model Summary tab. The Summary tab displays the Training Results and Additional Training Results metrics, calculated on the validation set.

Train a Gaussian naive Bayes model and a kernel naive Bayes model simultaneously. On the Learn tab, in the Models gallery, click All Naive Bayes. In the Train section, click Train All and select Train Selected.

The app trains one of each naive Bayes model type and highlights the Accuracy (Validation) value of the best model or models. Classification Learner displays a validation confusion matrix for the first model (model 4.1).

In the Models pane, click a model to view the results. For example, select model 2. Examine the scatter plot for the trained model. On the Learn tab, in the Plots and Results section, click the arrow to open the gallery, and then click Scatter in the Validation Results group. Try plotting different predictors. Misclassified points appear as an X.

Inspect the accuracy of the predictions in each class. 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. The app displays a matrix of true class and predicted class results.

In the Models pane, click the other trained models and compare their results.

To try to improve the models, include different features during model training. See if you can improve the models by removing features with low predictive power.

On the Learn tab, in the Options section, click Feature Selection.

In the Default Feature Selection tab, you can select different feature ranking algorithms to determine the most important features. After you select a feature ranking algorithm, the app displays a plot of the sorted feature importance scores, where larger scores (including Infs) indicate greater feature importance. The table shows the ranked features and their scores.

In this example, use one-way ANOVA to rank the features. Under Feature Ranking Algorithm, click ANOVA.

Under Feature Selection, use the default option of selecting the highest ranked features to avoid bias in the validation metrics. Specify to keep 2 of the 4 features for model training. Click Save and Apply. The app applies the feature selection changes to new models created using the Models gallery.

Train new naive Bayes models using the reduced set of features. On the Learn tab, in the Models gallery, click All Naive Bayes. In the Train section, click Train All and select Train Selected.

In this example, the two models trained using a reduced set of features perform better than the models trained using all the predictors. If data collection is expensive or difficult, you might prefer a model that performs satisfactorily without some predictors.

To determine which predictors are included, click a model in the Models pane, and note the check boxes in the expanded Feature Selection section of the model Summary tab. For example, model 5.1 contains only the petal measurements.

To further investigate features to include or exclude, use the parallel coordinates plot. On the Learn tab, in the Plots and Results section, click the arrow to open the gallery, and then click Parallel Coordinates in the Validation Results group.

In the Models pane, click the model with the highest Accuracy (Validation) value. To try to improve the model further, change its hyperparameters (if possible). First, duplicate the model by right-clicking the model and selecting Duplicate. Then, try changing hyperparameter settings in the model Summary tab. Recall that hyperparameter options are available only for some models. Train the new model by clicking Train All and selecting Train Selected in the Train section.

Export 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, click OK to accept the default variable name.

Examine the code for training this classifier. On the Learn tab, in the Export section, click Export Model and select Generate Function.

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

In the Get Started group, click All.

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