回归学习器
以交互方式训练、验证和调整回归模型
可以选择各种算法来训练和验证回归模型。训练多个模型后,可以横向比较它们的验证误差,然后选择最佳模型。要帮助您确定使用哪种算法,请参阅Train Regression Models in Regression Learner App。
此流程图显示在回归学习器中训练回归模型的常见工作流。

App
回归学习器 | Train regression models to predict data using supervised machine learning |
主题
常见工作流
- 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. - 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.
自定义工作流
- 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. - 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.