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, naive Bayes,
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.
Tip
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™.
Open the Classification Learner App
MATLAB Toolstrip: On the Apps tab, under
Machine Learning, click the app icon.
MATLAB command prompt: Enter classificationLearner.
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