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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回归学习者应用中的火车回归模型.

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

Apps

回归学习者

Train regression models to predict data using supervised machine learning

特姆斯

Flujo de Trabajo Frecuente

回归学习者应用中的火车回归模型
Workflow for training, comparing and improving regression models, including automated, manual, and parallel training.
选择回归或打开保存的应用程序会话的数据
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.
评估回归学习者中的模型性能
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 toMATLAB Production Server™.
Train Regression Trees Using Regression Learner App
Create and compare regression trees, and export trained models to make predictions for new data.
使用回归学习者应用程序的火车回归神经网络
Create and compare regression neural networks, and export trained models to make predictions for new data.

Flujo de trabajo personalizado

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.
回归学习者应用程序中的超参数优化
Automatically tune hyperparameters of regression models by using hyperparameter optimization.
Train Regression Model Using Hyperparameter Optimization in Regression Learner App
使用优化的超参数训练回归合奏模型。
使用回归学习者应用中的测试集检查模型性能
将测试集导入回归学习者，并检查测试集指标是否表现最好的训练有素。
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 toMATLAB Production Server.

InformaciónRoracionada

机器学习在MATLAB