App Regression Learner

Entrene, valide y ajuste modelos de regresión de forma interactiva

Elija entre distintos algoritmos para entrenar y validar modelos de regresión. Tras entrenar varios modelos, compare los errores de validación de forma directa y, después, elija el mejor modelo. Para decidir qué algoritmo usar, consulte Train Regression Models in Regression Learner App.

Este diagrama de flujo muestra un flujo de trabajo frecuente para entrenar modelos de regresión en la app Regression Learner.

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.

Si desea hacer experimentos con uno de los modelos que ha entrenado en Regression Learner, puede exportar el modelo a la app Experiment Manager. Para obtener más información, consulte Export Model from Regression Learner to Experiment Manager.

Apps

Regression Learner	Entrenar modelos de regresión para predecir datos usando machine learning supervisado
Experiment Manager	Design and run experiments to train and compare machine learning models (desde R2023a)

Temas

Flujo de trabajo frecuente

Train Regression Models in Regression Learner App
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
Seleccionar datos para regresión o abrir una sesión guardada en la app
Importe datos en Regression Learner desde el espacio de trabajo o desde archivos, encuentre conjuntos de datos de ejemplo, elija opciones de validación cruzada o validación por retención y reserve datos para las pruebas. También puede abrir una sesión de la app previamente guardada.
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.
Visualizar y evaluar el rendimiento de modelos en Regression Learner
Compare las métricas de los modelos y visualice los resultados.
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.
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.

Flujo de trabajo personalizado

Selección y transformación de características mediante la app Regression Learner
Identifique predictores útiles utilizando gráficas o algoritmos de clasificación de características, seleccione las características que desee incluir y transfórmelas con el PCA en 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.
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 partial dependence plots, LIME values, and Shapley values.
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.

Flujo de trabajo de 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.

Información relacionada

Machine learning en MATLAB
Gestionar experimentos (Deep Learning Toolbox)