회귀 학습기 앱

회귀 모델을 대화형 방식으로 훈련, 검증, 조정

다양한 알고리즘 중에서 회귀 모델을 훈련시키고 검증할 알고리즘을 선택할 수 있습니다. 여러 모델을 훈련시킨 후 검증 오차를 나란히 비교한 다음 최적의 모델을 선택합니다. 어떤 알고리즘을 사용할지 결정하는 데 도움이 필요하다면 Train Regression Models in Regression Learner App 항목을 참조하십시오.

다음 플로우 차트는 회귀 학습기 앱에서 회귀 모델을 훈련시키는 일반적인 워크플로를 보여줍니다.

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.

앱

회귀 학습기

머신러닝 지도 학습을 사용하여 데이터를 예측하도록 회귀 모델 훈련시키기

도움말 항목

일반 워크플로

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 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.
Train Kernel Approximation Model Using Regression Learner App
Create and compare kernel approximation models, and export trained models to make predictions for new data.

사용자 지정 워크플로

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.

회귀 학습기 앱

앱

도움말 항목

일반 워크플로

사용자 지정 워크플로

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