Machine learning methods have the potential to solve the challenges that organizational researchers face in terms of model specification and interpretation. As a machine learning algorithm learns the patterns from data without hard-coding fixed rules, it often makes very accurate predictions. However, as these models are often highly non-linear, it poses challenges for interpretation. This project aims to consolidate methods for interpreting machine learning models into a practical resource that makes them available and accessible to organizational scholars.