Over the past decade, focus on data science and machine learning (ML) has been radically shifted from theory to real-world applications
as more powerful machines, learning algorithms and the vast amount of data is available. The improved performance of ML algorithms
is the direct result of increased complexity. A prime example is the deep learning paradigm, where complex hierarchical models are built
by iteratively adding activation layers. This clearly states a trade-off between the performance of a machine learning model and
its ability to produce explainable and interpretable predictions. Therefore, on the one-hand, we have the so called **black-box models**
where the complex structure hinders model interpretability, such as deep learning and ensembles. On the other hand, there is the class of so
called **white-box** or **glass-box models**, which easily produce explainable results, such as
linear and decision tree based models.