Regression Learner App
Interactively train, validate, and tune regression models
Choose among various algorithms to train and validate regression models. After training multiple models, compare their validation errors side-by-side, and then choose the best model. To help you decide which algorithm to use, see Train Regression Models in Regression Learner App.
This flow chart shows a common workflow for training regression models in the Regression Learner app.
If you want to run experiments using one of the models you trained in Regression Learner, you can export the model to the Experiment Manager app. For more information, see Export Model from Regression Learner to Experiment Manager.
To learn how to train and validate classification models, see Classification Learner.
Apps
| Regression Learner | Train regression models to predict data using supervised machine learning |
| Experiment Manager | Create and run experiments to train and compare machine learning models (Since R2023a) |
Topics
Common Workflow
- Start a Classification Learner or Regression Learner Session
Start an app session by importing data from a file or the workspace, or by opening a saved app session. - Select Validation Scheme in Classification Learner or Regression Learner
Select a validation scheme to examine the predictive accuracy of models that you train. - Train Regression Models in Regression Learner App
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training. - Choose Model Options In Regression Learner
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, kernel approximation models, ensembles of regression trees, and regression neural networks. - Import Trained Model from Workspace into Classification Learner or Regression Learner
Import a trained model, including its training data, from the workspace at the start of a new session, or import a compatible trained model during the current session. (Since R2026a) - Train Regression Trees Using Regression Learner App
Create and compare regression trees, and export trained models to make predictions for new data.
Customized Workflow
- Feature Selection and Feature Transformation Using Regression Learner App
Identify useful predictors using plots or feature ranking algorithms, 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. - Edit Customizable Neural Network Using Network Editor in Classification Learner or Regression Learner
Edit a customizable neural network using the Network Editor, and then train the model and use training progress plots to check for overfitting. (Since R2026a)
Assess Model Performance
- Visualize and Assess Model Performance in Regression Learner
Compare model metrics and visualize results. - Compare Linear Regression Models Using Regression Learner App
Create an efficiently trained linear regression model and then compare it to a linear regression model. Export the efficient linear regression model to make predictions on new data. - Use Partial Dependence Plots to Interpret Regression Models Trained in Regression Learner App
Determine how features are used in trained regression models by creating partial dependence plots. - Test Trained Models in Classification Learner or Regression Learner
Test trained models to assess performance in real-world scenarios with unseen data. - Check Model Performance Using Test Data Set in Regression Learner App
Import a test set into Regression Learner, and check the test set metrics for the best-performing trained models. - Explain Model Predictions for Regression Models Trained in Regression Learner App
To understand how trained regression models use predictors to make predictions, use global and local interpretability tools, such as permutation importance plots, partial dependence plots, LIME values, and Shapley values.
Export Models, Partitions, Data Sets, and Plots
- Export Regression Model to Predict New Data
After training a model in Regression Learner, export the model to the workspace to make predictions on new data, and deploy the model to MATLAB® Compiler™. - Export Regression Model to Make Predictions in Simulink
After training a model in Regression Learner, export the model to Simulink®. - Export Regression Model to MATLAB Coder to Generate C/C++ Code
After training a model in Regression Learner, export the model to MATLAB Coder™ to generate C/C++ code for prediction. - Generate MATLAB Code to Train Model with New Data
After training a model in Regression Learner, generate MATLAB code. - Export Regression Model for Deployment to MATLAB Production Server
After training a model in Regression Learner, export the model for deployment to MATLAB Production Server™. - Deploy Model Trained in Regression Learner to MATLAB Production Server
Train a model in Regression Learner and export it for deployment to MATLAB Production Server. - Export Partitions and Data Sets from Classification Learner or Regression Learner
In Classification Learner and Regression Learner, export validation partitions, test partitions, and data sets to the workspace. (Since R2026a) - Export Plots in Regression Learner App
Export and customize plots created before and after training.
Experiment Manager Workflow
- Export Model from Regression Learner to Experiment Manager
Export a regression model to Experiment Manager to perform multiple experiments. - Tune Regression Model Using Experiment Manager
Use different training data sets, hyperparameters, and visualizations to tune a Gaussian process regression (GPR) model in Experiment Manager.
Related Information
- Machine Learning in MATLAB
- Manage Experiments (Deep Learning Toolbox)
Teaching Resources
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Learn the basics of machine learning with biologically motivated examples.
