Abstract¶
As Machine Learning (ML) becomes more widely known and popular, so too does the desire for new users from other backgrounds to apply ML techniques to their own domains. A difficult prerequisite that often confounds new users is the feature creation and engineering process. This is especially true when users attempt to apply ML to domains that have not historically received attention from the ML community (e.g., outside of text, images, and audio). The Lempel Ziv Jaccard Distance (LZJD) is a compression based technique that can be used for many machine learning tasks. Because of its compression background, users do not need to specify any feature extraction, making it easy to apply to new domains. We introduce PyLZJD, a library that implements LZJD in a manner meant to be easy to use and apply for novice practitioners. We will discuss the intuition and high-level mechanics behind LZJD, followed by examples of how to use it on problems of disparate data types.
