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분류 학습기 앱
대화형 방식으로 분류 모델 훈련, 검증 및 조정
이진 문제 또는 다중클래스 문제에 대해 분류 모델을 훈련시키고 검증하는 다양한 알고리즘 중에서 선택할 수 있습니다. 여러 모델을 훈련시킨 후 검증 오차를 나란히 비교한 다음 최적의 모델을 선택합니다. 어떤 알고리즘을 사용할지 결정하는 데 도움이 필요하다면분류 학습기 앱에서 분류 모델을 훈련시키기항목을참조하십시오。
다음 플로우 차트는 분류 학습기 앱에서 분류 모델 또는 분류기를 훈련시키는 일반적인 워크플로를 보여줍니다.
앱
| 분류 학습기 | 머신러닝 지도 학습을 사용하여 데이터를 분류하도록 모델 훈련시키기 |
도움말 항목
일반 워크플로
- 분류 학습기 앱에서 분류 모델을 훈련시키기
자동화된 훈련, 수동 훈련, 병렬 훈련 등 분류 모델을 훈련시키고 비교하고 향상시킬 수 있는 워크플로입니다. - Select Data for Classification or Open Saved App Session
Import data into Classification 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 Classifier Options
In Classification Learner, automatically train a selection of models, or compare and tune options in decision tree, discriminant analysis, logistic regression, naive Bayes, support vector machine, nearest neighbor, kernel approximation, ensemble, and neural network models. - Assess Classifier Performance in Classification Learner
Compare model accuracy scores, visualize results by plotting class predictions, and check performance per class in the Confusion Matrix. - Export Classification Model to Predict New Data
After training in Classification Learner, export models to the workspace, generate MATLAB®code, generate C code for prediction, or export models for deployment toMATLAB生产服务器™. - Train Decision Trees Using Classification Learner App
Create and compare classification trees, and export trained models to make predictions for new data. - Train Discriminant Analysis Classifiers Using Classification Learner App
Create and compare discriminant analysis classifiers, and export trained models to make predictions for new data. - Train Logistic Regression Classifiers Using Classification Learner App
Create and compare logistic regression classifiers, and export trained models to make predictions for new data. - Train Naive Bayes Classifiers Using Classification Learner App
Create and compare naive Bayes classifiers, and export trained models to make predictions for new data. - Train Support Vector Machines Using Classification Learner App
Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data. - Train Nearest Neighbor Classifiers Using Classification Learner App
Create and compare nearest neighbor classifiers, and export trained models to make predictions for new data. - Train Kernel Approximation Classifiers Using Classification Learner App
Create and compare kernel approximation classifiers, and export trained models to make predictions for new data. - Train Ensemble Classifiers Using Classification Learner App
Create and compare ensemble classifiers, and export trained models to make predictions for new data. - Train Neural Network Classifiers Using Classification Learner App
Create and compare neural network classifiers, and export trained models to make predictions for new data.
사용자 지정 워크플로
- Feature Selection and Feature Transformation Using Classification Learner App
Identify useful predictors using plots or feature ranking algorithms, select features to include, and transform features using PCA in Classification Learner. - Misclassification Costs in Classification Learner App
Before training any classification models, specify the costs associated with misclassifying the observations of one class into another. - Train and Compare Classifiers Using Misclassification Costs in Classification Learner App
Create classifiers after specifying misclassification costs, and compare the accuracy and total misclassification cost of the models. - Hyperparameter Optimization in Classification Learner App
Automatically tune hyperparameters of classification models by using hyperparameter optimization. - Train Classifier Using Hyperparameter Optimization in Classification Learner App
Train a classification support vector machine (SVM) model with optimized hyperparameters. - Check Classifier Performance Using Test Set in Classification Learner App
Import a test set into Classification Learner, and check the test set metrics for the best-performing trained models. - Export Plots in Classification Learner App
Export and customize plots created before and after training. - Code Generation and Classification Learner App
Train a classification model using the Classification Learner app, and generate C/C++ code for prediction. - Code Generation for Logistic Regression Model Trained in Classification Learner
This example shows how to train a logistic regression model using Classification Learner, and then generate C code that predicts labels using the exported classification model. - Deploy Model Trained in Classification Learner to MATLAB Production Server
Train a model in Classification Learner and export it for deployment toMATLAB生产服务器.
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