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Machine Learning (ML) is being used in multiple disciplines due to itspowerful capability to infer relationships within data. In particular, SoftwareEngineering (SE) is one of those disciplines in which ML has been used formultiple tasks, like software categorization, bugs prediction, and testing. Inaddition to the multiple ML applications, some studies have been conducted todetect and understand possible pitfalls and issues when using ML. However, tothe best of our knowledge, only a few studies have focused on presenting MLbest practices or guidelines for the application of ML in different domains. Inaddition, the practices and literature presented in previous literature (i) aredomain-specific (e.g., concrete practices in biomechanics), (ii) describe fewpractices, or (iii) the practices lack rigorous validation and are presented ingray literature. In this paper, we present a study listing 127 ML bestpractices systematically mining 242 posts of 14 different Stack Exchange (STE)websites and validated by four independent ML experts. The list of practices ispresented in a set of categories related to different stages of theimplementation process of an ML-enabled system; for each practice, we includeexplanations and examples. In all the practices, the provided examples focus onSE tasks. We expect this list of practices could help practitioners tounderstand better the practices and use ML in a more informed way, inparticular newcomers to this new area that sits at the intersection of softwareengineering and machine learning.

What are the Machine Learning best practices reported by practitioners on Stack Exchange?