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Train Regression Models in Regression Learner App

Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.

Select Data and Validation for Regression Problem

Import data into Regression Learner from the workspace or files, find example data sets, and choose cross-validation or holdout validation options.

Choose Regression Model Options

In Regression Learner, automatically train a selection of models, or compare and tune options of linear regression models, regression trees, support vector machines, Gaussian process regression models, ensembles of regression trees, and regression neural networks.

Assess Model Performance in Regression Learner

Compare model statistics and visualize results.

Export Regression Model to Predict New Data

After training in Regression Learner, export models to the workspace, generate MATLAB^® code, or generate C code for prediction.

Train Regression Trees Using Regression Learner App

Create and compare regression trees, and export trained models to make predictions for new data.

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Feature Selection and Feature Transformation Using Regression Learner App

Identify useful predictors using plots, manually select features to include, and transform features using PCA in Regression Learner.

Hyperparameter Optimization in Regression Learner App

Automatically tune hyperparameters of regression models by using hyperparameter optimization.

Train Regression Model Using Hyperparameter Optimization in Regression Learner App

Train a regression ensemble model with optimized hyperparameters.

Export Plots in Regression Learner App

Export and customize plots created before and after training.