In the MATLAB^® Command Window, load the fisheriris data set, and create a table from the variables in the data set to use for classification.

fishertable = readtable("fisheriris.csv");

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 fishertable from the Data Set Variable list (if necessary). Observe that the app has selected response and predictor variables based on their data types. 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 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 species and measurements. Note which variables separate the species colors most clearly.

Create a selection of neural network models. On the Learn tab, in the Models section, click the arrow to open the gallery. In the Neural Network Classifiers group, click All Neural Networks.

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

Classification Learner trains one of each neural network classification 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 neural network model (Narrow Neural Network).

Select a model in the Models pane to view the results. For example, double-click the Narrow Neural Network model (model 2.1). Inspect the model Summary tab, which displays the Training Results and Additional Training Results metrics, calculated on the validation set.

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. Correctly classified points are marked with an O, and incorrectly classified points are marked with 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. View the matrix of true class and predicted class results.

For each remaining model, select the model in the Models pane, open the validation confusion matrix, and then compare the results across the models.

Choose the best model in the Models pane (the best 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 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 model in the Models pane. To try to improve the model further, change its hyperparameters. First, duplicate the best model by right-clicking the model and selecting Duplicate. Then, try changing hyperparameter settings, like the sizes of the fully connected layers or the regularization strength, 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 neural network model settings, see Neural Network Classifiers.

You can export a full or compact version of the trained model to the workspace. On the Learn tab, in the Export section, click Export Model and select Export Model. To exclude the training data and export a compact model, clear the check box in the Export Classification Model dialog box. You can still use the compact model for making predictions on new 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.

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.