Choose between 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.
|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, and ensembles of regression trees.
Identify useful predictors using plots, manually select features to include, and transform features using PCA in Regression Learner.
Compare model statistics and visualize results.
Export and customize plots created before and after training.
After training In Regression Learner, export models to the workspace or generate MATLAB® code.
Create and compare regression trees, and export trained models to make predictions for new data.