matlab训练器,分类学习器 - MATLAB & Simulink - MathWorks 中国

常见工作流

Workflow for training, comparing and improving classification models,

including automated, manual, and parallel training.

Import data into Classification Learner from the workspace or files, find

example data sets, and choose cross-validation or holdout validation

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, ensemble, and

neural network models.

Compare model accuracy scores, visualize results by plotting class

predictions, and check performance per class in the Confusion Matrix.

After training in Classification Learner, export models to the workspace,

generate MATLAB® code, or generate C code for prediction.

Create and compare classification trees, and export trained models to make

predictions for new data.

Create and compare discriminant analysis classifiers, and export trained

models to make predictions for new data.

Create and compare logistic regression classifiers, and export trained models

to make predictions for new data.

Create and compare naive Bayes classifiers, and export trained models to make

predictions for new data.

Create and compare support vector machine (SVM) classifiers, and export

trained models to make predictions for new data.

Create and compare nearest neighbor classifiers, and export trained models to

make predictions for new data.

Create and compare ensemble classifiers, and export trained models to make

predictions for new data.