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.
|Regression Learner||Train regression models to predict data using supervised machine learning|
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
Import data into Regression Learner from the workspace or files, find example data sets, and choose cross-validation or holdout validation 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.
Compare model statistics and visualize results.
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™.
Create and compare regression trees, and export trained models to make predictions for new data.
Create and compare regression neural networks, and export trained models to make predictions for new data.
Identify useful predictors using plots, manually select features to include, and transform features using PCA in Regression Learner.
Automatically tune hyperparameters of regression models by using hyperparameter optimization.
Train a regression ensemble model with optimized hyperparameters.
Import a test set into Regression Learner, and check the test set metrics for the best-performing trained models.
Export and customize plots created before and after training.
Train a model in Regression Learner and export it for deployment to MATLAB Production Server.