Explainable AI with Linear Trees

In the field of Artificial Intelligence, the trade-off between accuracy and interpretability is a crucial aspect when developing a machine learning pipeline. Accuracy refers to the correctness degree of model predictions, while interpretability specifies how easy is the human understanding of the results. The balance between the two is related to the final business needs. When we try to explain the output of our ML pipeline we should consider a crucial aspect. Interpretability may not result in explainability.