常见工作流

Train Classification Models in Classification Learner App

Workflow for training, comparing and improving classification models, including automated, manual, and parallel training.

Select Data and Validation for Classification Problem

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

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 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.

自定义工作流

Feature Selection and Feature Transformation Using Classification Learner App

Identify useful predictors using plots, manually 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.