This week in Wayfair Data Science’s Explainer Series, Data Science Tech Lead Peter B. Golbus discusses machine learning from a theoretical computer science perspective. In this video, we describe multiclass classification as an encoding task, i.e. a process for building compression schemes that convert large "files" (feature vectors) into small ones (labels). By framing classification this way, we are able to use the powerful tools of Information Theory to produce actionable insight. In particular, we discuss that classification accuracy is bounded from above by the mutual information between your features and labels, and how information theory explains why ensembling and feature selection are such powerful tools for machine learning.