Handling the data imbalance problem is one of the crucial steps in a machine learning pipeline. The research community is well aware of the effects of data imbalance on machine learning algorithms. At the same time, there is a rising need for explainability of AI, especially in difficult, high-stake domains like network intrusion detection. In this paper, the effects of data balancing procedures on two explainability procedures implemented to explain a neural network used for network intrusion detection are evaluated. The discrepancies between the two methods are highlighted and important conclusions are drawn.