App Regressione Learner
Addestrare, validare e ottimizzare i modelli di regressione in modo interattivo
Scegliere un algoritmo tra quelli disponibili per addestrare e validare i modelli di regressione. Dopo l'addestramento di più modelli, confrontare i loro errori di validazione affiancati, quindi scegliere il modello migliore. Per un supporto nella scelta dell'algoritmo da utilizzare, vedere Train Regression Models in Regression Learner App.
Questo diagramma di flusso illustra un workflow tipico per l'addestramento dei modelli di regressione nell'app Regression Learner.
Se si desidera eseguire esperimenti utilizzando uno dei modelli addestrati in Regression Learner, è possibile esportare il modello nell'app Experiment Manager. Per ulteriori informazioni, vedere Export Model from Regression Learner to Experiment Manager.
Per scoprire come addestrare e validare i modelli di classificazione, vedere Classification Learner.
App
| Regression Learner | Train regression models to predict data using supervised machine learning |
| Experiment Manager | Create and run experiments to train and compare machine learning models (Da R2023a) |
Argomenti
Workflow tipico
- Start a Classification Learner or Regression Learner Session
Start an app session by importing data from a file or the workspace, or by opening a saved app session. - Select Validation Scheme in Classification Learner or Regression Learner
Select a validation scheme to examine the predictive accuracy of models that you train. - Train Regression Models in Regression Learner App
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training. - Choose Model Options In Regression Learner
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. - Import Trained Model from Workspace into Classification Learner or Regression Learner
Import a trained model, including its training data, from the workspace at the start of a new session, or import a compatible trained model during the current session. (Da R2026a) - Train Regression Trees Using Regression Learner App
Create and compare regression trees, and export trained models to make predictions for new data.
Workflow personalizzato
- 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. - Edit Customizable Neural Network Using Network Editor in Classification Learner or Regression Learner
Edit a customizable neural network using the Network Editor, and then train the model and use training progress plots to check for overfitting. (Da R2026a)
Valutazione delle prestazioni del modello
- Visualize and Assess Model Performance in Regression Learner
Compare model metrics and visualize results. - Compare Linear Regression Models Using Regression Learner App
Create an efficiently trained linear regression model and then compare it to a linear regression model. Export the efficient linear regression model to make predictions on new data. - 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. - Test Trained Models in Classification Learner or Regression Learner
Test trained models to assess performance in real-world scenarios with unseen data. - Check Model Performance Using Test Data Set in Regression Learner App
Import a test set into Regression Learner, and check the test set metrics for the best-performing trained models. - Explain Model Predictions for Regression Models Trained in Regression Learner App
To understand how trained regression models use predictors to make predictions, use global and local interpretability tools, such as permutation importance plots, partial dependence plots, LIME values, and Shapley values.
Esportazione di modelli, partizioni, insiemi di dati e grafici
- Export Regression Model to Predict New Data
After training a model in Regression Learner, export the model to the workspace to make predictions on new data, and deploy the model to MATLAB® Compiler™. - Export Regression Model to Make Predictions in Simulink
After training a model in Regression Learner, export the model to Simulink®. - Export Regression Model to MATLAB Coder to Generate C/C++ Code
After training a model in Regression Learner, export the model to MATLAB Coder™ to generate C/C++ code for prediction. - Generate MATLAB Code to Train Model with New Data
After training a model in Regression Learner, generate MATLAB code. - Export Regression Model for Deployment to MATLAB Production Server
After training a model in Regression Learner, export the model for deployment to MATLAB Production Server™. - 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. - Export Partitions and Data Sets from Classification Learner or Regression Learner
In Classification Learner and Regression Learner, export validation partitions, test partitions, and data sets to the workspace. (Da R2026a) - Export Plots in Regression Learner App
Export and customize plots created before and after training.
Workflow di Experiment Manager
- 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.
Informazioni complementari
- Machine Learning in MATLAB
- Gestione degli esperimenti (Deep Learning Toolbox)
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