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 필요하다면 분류 학습기 앱에서 분류 모델을 훈련시키기 항목을 참조하십시오.

다음 플로우 차트는 분류 학습기 앱에서 분류 모델 또는 분류기를 훈련시키는 일반적인 워크플로를 보여줍니다.

Workflow in the Classification Learner app. Step 1: Select data and validation. Step 2: Choose classifier options. Step 3: Train a classifier. Step 4: Assess classifier performance. Step 5: Export the classifier.

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 to MATLAB Production Server™.
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 to MATLAB Production Server.

분류 학습기 앱

앱

도움말 항목

일반 워크플로

사용자 지정 워크플로

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