A word about black boxes Nowadays a fierce competition can be observed – scientists are surpassing each other in creating better regression models. As those models are getting more complex, it is becoming almost impossible to illustrate results relation with data, in a way humans understand. They are commonly called ‘black boxes’. ‘Machine learning is frequently referred to as a black box—data goes in, decisions come out, but the processes between input and output are opaque’ ~ The Lancet. Despite their excellent performance, sometimes models with easily interpretable output can be more desired, e.g. in banking. What can be done? Results ready for further human analysis can be achieved with explainable models (linear models, decision trees, etc.

Black vs white Machine learning seems to be all about creating a model with best performance - balancing well its variance and accuracy. Unfortunately, the pursuit of that balance makes us forget about the the fact, that - in the end - model will serve human beings. If that’s the case, a third factor should be considered - interpretability. When a model is unexplainable (AKA black-box model), it may be treated as untrustworthy and become useless. It is a problem, since many models known for its high performance (like XGBoost) happen to be parts of the black-box team. A false(?) trade-off So it would seem, that explainability is, and has to be, sacrificed for better performance of the model.