5.8 Scoped Rules (Anchors) Interpretable Machine Learning

Ignoring hyperparameters helps reduce the boundary's complexity (see original paper for more info). Since the MAB extracts the \(B\) best out of \(B \cdot p\) candidates in each round, most MABs and their runtimes multiply the \(p 2\) factor more than any other parameter. It thus becomes apparent: the algorithm's efficiency decreases with feature abundant problems. Tabular data is structured data represented by tables, wherein columns embody features and rows instances. For instance, we use the bike rental data to demonstrate the anchors approach's potential to explain ML predictions for selected instances. For this, we turn the regression into a classification problem and train a random forest as our black-box model.