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GazeML is a python application that runs machine learning experiment to test the classification of user characteristics from eye-tracking data.

GazeML reads in existing features generated from eye-tracking data in an experiment (e.g., by using EMDAT) and runs cross validations to test the performance of different feature sets and classifiers. It is built on the scikit-learn machine learning library.

When given gaze movement sequence data, GazeML can extract patterns from the sequences and compute features based on the frequency of the patterns.