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。

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.

如果您要使用您在回归学习器中训练的模型之一来运行试验，您可以将该模型导出为试验管理器。有关详细信息，请参阅Export Model from Regression Learner to Experiment Manager。

App

回归学习器	使用有监督机器学习训练回归模型来预测数据
试验管理器

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.
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.

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.
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.

试验管理器工作流

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.

回归学习器

App

主题

常见工作流

自定义工作流

试验管理器工作流

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