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 회귀 학습기 앱에서 회귀 모델 훈련시키기 항목을 참조하십시오.
다음 플로우 차트는 회귀 학습기 앱에서 회귀 모델을 훈련시키는 일반적인 워크플로를 보여줍니다.
회귀 학습기에서 학습시킨 모델 중 하나를 사용하여 실험을 실행하려는 경우 해당 모델을 실험 관리자 앱으로 내보낼 수 있습니다. 자세한 내용은 Export Model from Regression Learner to Experiment Manager 항목을 참조하십시오.
분류 모델을 훈련시키고 검증하는 방법을 알아보려면 분류 학습기 항목을 참조하십시오.
앱
도움말 항목
일반 워크플로
- 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. - 회귀 학습기 앱에서 회귀 모델 훈련시키기
자동화된 훈련, 수동 훈련, 병렬 훈련 등 회귀 모델을 훈련시키고 비교하고 향상시킬 수 있는 워크플로입니다. - 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. (R2026a 이후) - 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
- 회귀 학습기 앱을 사용한 특징 선택과 특징 변환
회귀 학습기에서 플롯 또는 특징 순위 지정 알고리즘을 사용하여 유용한 예측 변수를 식별하고, 포함할 특징을 선택하고, PCA를 사용하여 특징을 변환합니다. - 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. (R2026a 이후)
모델 성능 평가하기
- 회귀 학습기의 모델 성능 시각화 및 평가하기
모델 메트릭을 비교하고 결과를 시각화합니다. - 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.
모델, 분할, 데이터 세트, 플롯 내보내기
- 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. (R2026a 이후) - Export Plots in Regression Learner App
Export and customize plots created before and after training.
실험 관리자 워크플로
- 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.
관련 정보
- MATLAB의 머신러닝
- 실험 관리하기 (Deep Learning Toolbox)
교육 관련 자료
생명과학 분야의 머신러닝
생물학적 사례를 기반으로 머신러닝의 기본 사항을 알아봅니다.
