Classification Learner

Train models to classify data using supervised machine learning

Description

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 optimize hyperparameters, assess results, and investigate how specific predictors contribute to model predictions. Perform automated training to search for the best classification model type, including decision trees, discriminant analysis models, support vector machines, logistic regression models, nearest neighbors, naive Bayes models, kernel approximation models, ensemble models, and neural network classification models. To compare models, use the metric results table and view results plots in the app.

Perform supervised machine learning by supplying a known set of input data (observations or examples) and known responses to the data (labels or classes). Use the data to train a model that generates predictions for the response to new data. You can then check model performance using a test data set. To understand how the model uses predictors to make predictions, use global and local interpretability tools, such as partial dependence plots, LIME values, and Shapley values.

To use the trained model with new data, you can export the model to the workspace, Simulink^®, and MATLAB^® Production Server™. You can generate MATLAB code to recreate the trained model outside of the app and explore programmatic classification and further customization of the model training workflow. Export the model training code to Experiment Manager to perform additional tasks, such as changing the training data, adjusting hyperparameter search ranges, and running custom training experiments.

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™

Open the Classification Learner App

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

• MATLAB command prompt: Enter classificationLearner.

Examples

• Train Classification Models in Classification Learner App
• Select Data for Classification or Open Saved App Session
• Automated Classifier Training
• Feature Selection and Feature Transformation Using Classification Learner App
• Choose Classifier Options
• Visualize and Assess Classifier Performance in Classification Learner
• Explain Model Predictions for Classifiers Trained in Classification Learner App
• Export Classification Model to Predict New Data

Programmatic Use

classificationLearner

classificationLearner opens the Classification Learner app or brings focus to the app if it is already open.

classificationLearner(Tbl,ResponseVarName)

classificationLearner(Tbl,ResponseVarName) opens the Classification Learner app and populates the New Session from Arguments dialog box with the data contained in the table Tbl. The ResponseVarName argument, specified as a character vector or string scalar, is the name of the response variable in Tbl that contains the class labels. The response variable cannot contain more than 500 unique class labels. The remaining variables in Tbl are the predictor variables.

classificationLearner(Tbl,Y)

classificationLearner(Tbl,Y) opens the Classification Learner app and populates the New Session from Arguments dialog box with the predictor variables in the table Tbl and the class labels in the vector Y. You can specify the response Y as a categorical array, character array, string array, logical vector, numeric vector, or cell array of character vectors. Y cannot contain more than 500 unique class labels.

classificationLearner(X,Y)

classificationLearner(X,Y) opens the Classification Learner app and populates the New Session from Arguments dialog box with the n-by-p predictor matrix X and the n class labels in the vector Y. Each row of X corresponds to one observation, and each column corresponds to one variable. The length of Y and the number of rows of X must be equal. Y cannot contain more than 500 unique class labels.

classificationLearner(___,Name,Value)

classificationLearner(___,Name,Value) specifies cross-validation options using one or more of the following name-value arguments in addition to any of the input argument combinations in the previous syntaxes. For example, you can specify "KFold",10 to use a 10-fold cross-validation scheme.

• "CrossVal", specified as "on" (default) or "off", is the cross-validation flag. If you specify "on", then the app uses 5-fold cross-validation. If you specify "off", then the app uses resubstitution validation.

  You can override the "CrossVal" cross-validation setting by using the "Holdout" or "KFold" name-value argument. You can specify only one of these arguments at a time.

• "Holdout", specified as a numeric scalar in the range [0.05,0.5], is the fraction of the data used for holdout validation. The app uses the remaining data for training (and testing, if specified).

• "KFold", specified as a positive integer in the range [2,50], is the number of folds to use for cross-validation.

• "TestDataFraction", specified as a numeric scalar in the range [0,0.5], is the fraction of the data reserved for testing.

classificationLearner(filename)

classificationLearner(filename) opens the Classification Learner app with the previously saved session in filename. The filename argument, specified as a character vector or string scalar, must include the name of a Classification Learner session file and the path to the file, if it is not in the current folder. The file must have the extension .mat.

Limitations

• Classification Learner does not support model deployment to MATLAB Production Server in MATLAB Online™.

Version History

Introduced in R2015a