Statistics and Machine Learning Toolbox™ provides several anomaly detection features for unlabeled multivariate sample data. You can also detect anomalies in streaming data using one-class support vector machine (SVM) and robust random cut forest algorithms (see Incremental Anomaly Detection Overview). The anomaly detection features detect outliers (anomalies in training data) either by training a model or by learning parameters. For novelty detection (detecting anomalies in new data with uncontaminated training data), you train a model or learn parameters with uncontaminated training data (data with no outliers) and detect anomalies in new data by using the trained model or learned parameters. For more details, see Unsupervised Anomaly Detection.

If you have training data labeled as normal points and anomalies, you can train a binary classification model and use the resubPredict and predict object functions to detect anomalies in the training data and new data, respectively. For the list of supported classification features, see Classification.

The toolbox also provides model-specific anomaly detection features that you can apply after training a classification, regression, or clustering model. For details, see Model-Specific Anomaly Detection.