Train Classification Models in Classification Learner App

On the Apps tab, in the Machine Learning group, click Classification Learner.

Click New Session and select data from the workspace or from file. Specify a response variable and variables to use as predictors. See Select Data and Validation for Classification Problem.

On the Classification Learner tab, in the Model Type section, click All Quick-To-Train. This option will train all the model presets available for your data set that are fast to fit.

Click Train .

A selection of model types appears in the History list. When they finish training, the best percentage Accuracy score is highlighted in a box.

Click models in the history list to explore results in the plots.

For next steps, see Manual Classifier Training or Compare and Improve Classification Models.

To try all the nonoptimizable classifier model presets available for your data set, click All, then click Train.

Choose a classifier. On the Classification Learner tab, in the Model Type section, click a classifier type. To see all available classifier options, click the arrow on the far right of the Model Type section to expand the list of classifiers. The nonoptimizable model options in the Model Type gallery are preset starting points with different settings, suitable for a range of different classification problems.

To read a description of each classifier, switch to the details view.

For more information on each option, see Choose Classifier Options.

After selecting a classifier, click Train. 

Repeat to try different classifiers.

If you want to try all nonoptimizable models of the same or different types, then select one of the All options in the Model Type gallery.

Alternatively, if you want to automatically tune hyperparameters of a specific model type, select the corresponding Optimizable model and perform hyperparameter optimization. For more information, see Hyperparameter Optimization in Classification Learner App.

The first time you click Train, you see a dialog while the app opens a parallel pool of workers. After the pool opens, you can train multiple classifiers at once.

When classifiers are training in parallel, you see progress indicators on each training and queued model in the history list, and you can cancel individual models if you want. During training, you can examine results and plots from models, and initiate training of more classifiers.

Click models in the history list to explore the results in the plots. Compare model performance by inspecting results in the scatter plot and confusion matrix. Examine the percentage accuracy reported in the history list for each model. See Assess Classifier Performance in Classification Learner.

Select the best model in the history list and then try including and excluding different features in the model. Click Feature Selection. 

Try the parallel coordinates plot to help you identify features to remove. See if you can improve the model by removing features with low predictive power. Specify predictors to include in the model, and train new models using the new options. Compare results among the models in the history list.

You can also try transforming features with PCA to reduce dimensionality.

See Feature Selection and Feature Transformation Using Classification Learner App.

To improve the model further, you can try changing classifier parameter settings in the Advanced dialog box, and then train using the new options. To learn how to control model flexibility, see Choose Classifier Options. For information on how to tune model parameter settings automatically, see Hyperparameter Optimization in Classification Learner App.

If feature selection, PCA, or new parameter settings improve your model, try training All model types with the new settings. See if another model type does better with the new settings.