 In this paper we show how Explainable Artificial Intelligence (XAI) can be useful for quantifying the impact that different feature groups have on the fuel consumption of a particular fleet. For that, we use Explainable Boosting Machines (EBM) that are trained over different features (up to 70) in order to first model the relationship between them and the fuel consumption, and then explain it.
<clarity>  In this paper we show how Explainable Artificial Intelligence (XAI) can be useful for quantifying the impact that different feature groups have on the fuel consumption of a particular fleet. For that, we use Explainable Boosting Machines (EBM) that are trained over different features (up to 70) in order to first model the relationship between them and the fuel consumption, and then explain it.
 In this paper we show how Explainable Artificial Intelligence (XAI) can be useful for quantifying the impact that different feature groups have on the fuel consumption of a vehicle fleet. We use Explainable Boosting Machines (EBM) that are trained over different features (up to 70) in order to first model the relationship between them and the fuel consumption, and then explain it.