Regression Learner App
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.
- Train Regression Models in Regression Learner App
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
- Select Data for Regression or Open Saved App Session
Import data into Regression Learner from the workspace or files, find example data sets, choose cross-validation or holdout validation options, and set aside data for testing. Alternatively, open a previously saved app session.
- 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, kernel approximation models, ensembles of regression trees, and regression neural networks.
- Visualize and Assess Model Performance in Regression Learner
Compare model metrics and visualize results.
- Export Regression Model to Predict New Data
After training in Regression Learner, export models to the workspace, generate MATLAB® code, generate C code for prediction, or export models for deployment to MATLAB Production Server™.
- Train Regression Trees Using Regression Learner App
Create and compare regression trees, and export trained models to make predictions for new data.
- Train Regression Neural Networks Using Regression Learner App
Create and compare regression neural networks, and export trained models to make predictions for new data.
- Train Kernel Approximation Model Using Regression Learner App
Create and compare kernel approximation models, and export trained models to make predictions for new data.
- 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.
- 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.
- Check Model Performance Using Test 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 partial dependence plots, LIME values, and Shapley values.
- 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.
- Export Plots in Regression Learner App
Export and customize plots created before and after training.
- 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.
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.