Hybrid Rule-Based Machine Learning With scikit-learn

TL;DR scikit-learn does not allow you to add hard-coded rules to your machine learning model, but for many use cases, you should! This article explores how you can leverage domain knowledge and object-oriented programming (OOP) to build hybrid rule-based machine learning models on top of scikit-learn. Supervised machine learning models are great for making predictions under uncertainty; they pick up patterns in past data and accurately extrapolate them into the future. Machine learning has pushed the frontier in fields where determining the most likely outcome, whether a class or specific value, has historically been challenging, prone to error, or too time-consuming or expensive at scale. Still, there exist many domains in which some of all possible outcomes are not ambiguous but certain by definition.