Objectives: Machine learning models are a promising, yet underutilized tool within the mindfulness field. Accordingly, this work aimed to provide a practical introduction to key machine learning concepts through an illustrative investigation of the association between at-home mindfulness exercise compliance and stress reduction. To further interrogate the currently inconclusive nature of the compliance-outcome association within the mindfulness literature, the illustrative example leveraged a suite of machine learning techniques to highlight the unique affordances and perspectives of the predictive framework. Methods: Foundational information regarding facets of the machine learning analytical process, including model types, data preprocessing, feature engineering, validation, performance evaluation, and model introspection, was presented. With emphasis on providing details and justifications regarding modeling decisions along the way, the work systematically applied these introduced concepts to a real-world data example. This permitted an opportunity to build, introspect, and derive insight from a model tasked to explore dynamics underlying patient compliance to mindfulness exercises within a web-based delivery setting. Results: The constructed machine learning models suggested a moderate correlation of compliance with post-intervention reliable change in stress (r = 0.349 ± 0.018). Model introspection tools further revealed that a combination of both high consistency and high overall average compliance predicts a trend toward greater reduction in self-reported stress. Conclusions: Results of the illustrative study suggested that compliance, in pattern and absolute magnitude, is a significant contributor to online mindfulness therapy outcomes. Moreover, modeling efforts implicate machine learning as a uniquely beneficial paradigm with which to explore nuanced questions in the mindfulness research space.