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Classification Learner

Train models to classify data using supervised machine learning

Descripción

The Classification Learner app trains models to classify data. Using this app, you can explore supervised machine learning using various classifiers. You can explore your data, select features, specify validation schemes, train models, and assess results. You can perform automated training to search for the best classification model type, including decision trees, discriminant analysis, support vector machines, logistic regression, nearest neighbors, and ensemble classification.

You can perform supervised machine learning by supplying a known set of input data (observations or examples) and known responses to the data (e.g., labels or classes). You use the data to train a model that generates predictions for the response to new data. To use the model with new data, or to learn about programmatic classification, you can export the model to the workspace or generate MATLAB^® code to recreate the trained model.

Sugerencia

To get started, in the Classifier list, try All Quick-To-Train to train a selection of models. See Automated Classifier Training.

More

Required Products

• MATLAB

• Statistics and Machine Learning Toolbox™

Note: Classification Learner does not provide data import from file, code generation, or parallel model training in MATLAB Online™ .

Abrir la aplicación Classification Learner

• MATLAB Toolstrip: On the Apps tab, under Machine Learning, click the app icon.

• MATLAB command prompt: Enter classificationLearner.

Ejemplos

Ejemplos relacionados

• Train Classification Models in Classification Learner App
• Select Data and Validation for Classification Problem
• Automated Classifier Training
• Feature Selection and Feature Transformation Using Classification Learner App
• Choose Classifier Options
• Assess Classifier Performance in Classification Learner
• Export Classification Model to Predict New Data

Consulte también

Aplicaciones

• Regression Learner

Funciones

• fitcdiscr | fitcecoc | fitcensemble | fitcknn | fitcsvm | fitctree | fitglm

Introducido en R2015a