[D] Interpretable Machine Learning - Christoph Molnar (Video)

Christoph Molnar is one of the main people to know in the space of interpretable ML. In 2018 he released the first version of his incredible online book, interpretable machine learning. Interpretability is often a deciding factor when a machine learning (ML) model is used in a product, a decision process, or in research. Interpretability methods can be used to discover knowledge, to debug or justify the model and its predictions, and to control and improve the model, reason about potential bias in models as well as increase the social acceptance of models. But Interpretability methods can also be quite esoteric, add an additional layer of complexity and potential pitfalls and requires expert knowledge to understand.