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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.

회귀 학습기Train regression models to predict data using supervised machine learning

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 and Validation for Regression Problem

Import data into Regression Learner from the workspace or files, find example data sets, and choose cross-validation or holdout validation options.

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, and ensembles of regression trees.

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 or generate MATLAB® code.

Train Regression Trees Using Regression Learner App

Create and compare regression trees, 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, manually 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.

Export Plots in Regression Learner App

Export and customize plots created before and after training.

관련 정보