Interacting with features: Visual inspection of black-box fault type classification systems in electrical grids

Automatic fault type classification is an important ingredient of smart electrical grids. Similar to other machine-learning models, methods developed for fault classification suffer from the issue of lack of transparency. This work sheds light on preliminary insights of an ongoing study, in which we show how feature importance measurement and feature interaction visualization using partial dependence plots (PDPs) can help interpretability of the classification outcomes. While the former, measures the role of each feature on the final predictions in isolation, the latter focuses on mutual interaction between pairs of features. We show the merits of these two complementary feature analysis mechanisms in facilitating interpretability of the fault type classification task.