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.

분류 학습기에서 학습시킨 모델 중 하나를 사용하여 실험을 실행하려는 경우 해당 모델을 실험 관리자 앱으로 내보낼 수 있습니다. 자세한 내용은 Export Model from Classification Learner to Experiment Manager 항목을 참조하십시오.

앱

분류 학습기	머신러닝 지도 학습을 사용하여 데이터를 분류하도록 모델 훈련시키기
실험 관리자	Design and run experiments to train and compare machine learning models (R2023a 이후)

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.
분류할 데이터를 선택하거나 저장한 앱 세션 열기
작업 공간 또는 파일에서 분류 학습기로 데이터를 가져오고, 예제 데이터 세트를 찾고, 교차 검증 또는 홀드아웃 검증 옵션을 선택하고, 검정에 사용할 데이터를 별도로 남겨둡니다. 또는 이전에 저장한 앱 세션을 엽니다.
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.
분류 학습기의 분류기 성능 시각화 및 평가하기
모델 정확도 값을 비교하고, 클래스 예측값을 플로팅하여 결과를 시각화하고, 혼동행렬에서 클래스별 성능을 검사합니다.
Export Classification Model to Predict New Data
After training in Classification Learner, export models to the workspace and Simulink^®, 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 Binary GLM Logistic Regression Classifier Using Classification Learner App
Create and compare binary 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

분류 학습기 앱을 사용한 특징 선택 및 특징 변환
분류 학습기에서 플롯 또는 특징 순위 지정 알고리즘을 사용하여 유용한 예측 변수를 식별하고, 포함할 특징을 선택하고, PCA를 사용하여 특징을 변환합니다.
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.
Explain Model Predictions for Classifiers Trained in Classification Learner App
To understand how trained classifiers use predictors to make predictions, use global and local interpretability tools, such as partial dependence plots, LIME values, and Shapley values.
Use Partial Dependence Plots to Interpret Classifiers Trained in Classification Learner App
Determine how features are used in trained classifiers by creating partial dependence plots.
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 Binary GLM Logistic Regression Model Trained in Classification Learner
This example shows how to train a binary GLM 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.
Build Condition Model for Industrial Machinery and Manufacturing Processes
Train a binary classification model using Classification Learner App to detect anomalies in sensor data collected from an industrial manufacturing machine.

실험 관리자 워크플로

Export Model from Classification Learner to Experiment Manager
Export a classification model to Experiment Manager to perform multiple experiments.
Tune Classification Model Using Experiment Manager
Use different training data sets, hyperparameters, and visualizations to tune an efficient linear classifier in Experiment Manager.

분류 학습기 앱

앱

도움말 항목

일반 워크플로

사용자 지정 워크플로

실험 관리자 워크플로

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