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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.

Workflow in the Regression Learner app. Step 1: Select data and validation. Step 2: Choose regression model options. Step 3: Train a regression model. Step 4: Assess the regression model performance. Step 5: Export the regression model.

Apps

Regression LearnerTrain regression models to predict data using supervised machine learning

Topics

Common Workflow

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, 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.

Customized Workflow

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.

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.

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.

Related Information