Train Regression Models in Regression Learner App

On the Apps tab, in the Machine Learning group, click Regression 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 Regression Problem.

On the Regression Learner tab, in the Model Type section, click the arrow to expand the list of regression models. Select All Quick-To-Train. This option trains all the model presets that are fast to fit.

Click Train .

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

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

For the next steps, see Manual Regression Model Training or Compare and Improve Regression Models.

To try all the nonoptimizable model presets available, click All , and then click Train.

Choose a model type. On the Regression Learner tab, in the Model Type section, click a model type. To see all available model options, click the arrow in the Model Type section to expand the list of regression models. The nonoptimizable model options in the gallery are preset starting points with different settings, suitable for a range of different regression problems.

To read descriptions of the models, switch to the details view or hover the mouse over a button to display its tooltip.

For more information on each option, see Choose Regression Model Options.

After selecting a model, click Train .

Repeat to explore different models.

If you want to try all nonoptimizable models of the same or different types, then select one of the All options in the 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 Regression Learner App.

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

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

Click models in the History list to explore the results in the plots. Compare model performance by inspecting results in the plots. Examine the RMSE score reported in the History list for each model. See Assess Model Performance in Regression 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 response 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 also can try transforming features with PCA to reduce dimensionality.

See Feature Selection and Feature Transformation Using Regression Learner App.

Improve the model further by changing model parameter settings in the Advanced dialog box. Then, train using the new options. To learn how to control model flexibility, see Choose Regression Model Options. For information on how to tune model parameter settings automatically, see Hyperparameter Optimization in Regression 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.