Common Workflow

分类学习者应用中的火车分类模型

用于培训，比较和改进分类模型的工作流程，包括自动化，手动和并行培训。

Select Data and Validation for Classification Problem

将数据从工作区或文件中导入分类学习者，查找示例数据集，然后选择交叉验证或保留验证选项。

Choose Classifier Options

在分类学习者中，会自动训练模型的选择，或在决策树，判别分析，逻辑回归，天真的贝叶斯，支持向量机，最近的邻居，内核近似，集合和神经网络模型中进行比较和调整选项。金宝app

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 Production Server™.

使用分类学习者应用程序训练决策树

创建和比较分类树和导出训练有素的模型，以对新数据进行预测。

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.

使用分类学习金宝app者应用程序的火车支持向量机

Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.

使用分类学习者应用程序培训最近的邻居分类器

Create and compare nearest neighbor classifiers, and export trained models to make predictions for new data.

使用分类学习者应用程序训练内核近似分类器

创建和比较内核近似分类器，并导出经过训练的模型以对新数据进行预测。

Train Ensemble Classifiers Using Classification Learner App

Create and compare ensemble classifiers, and export trained models to make predictions for new data.

使用分类学习者应用程序培训神经网络分类器

Create and compare neural network classifiers, and export trained models to make predictions for new data.

Customized Workflow

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.

在分类学习者应用中使用错误分类成本进行培训和比较分类器

Create classifiers after specifying misclassification costs, and compare the accuracy and total misclassification cost of the models.

分类学习者应用程序中的超参数优化

通过使用超参数优化来自动调整分类模型的超参数。

Train Classifier Using Hyperparameter Optimization in Classification Learner App

使用优化的超参数训练分类支持向量机（SVM）模金宝app型。

Check Classifier Performance Using Test Set in Classification Learner App

将测试集导入分类学习者，并检查测试集指标是否表现最好的训练有素。

Export Plots in Classification Learner App

Export and customize plots created before and after training.

Code Generation and Classification Learner App

使用分类学习者应用训练分类模型，并生成C/C ++代码进行预测。

分类学习者培训的逻辑回归模型的代码生成

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.

部署在分类学习者中培训的模型到MATLAB生产服务器

Train a model in Classification Learner and export it for deployment toMATLAB Production Server.