  Neural Networks are known to be sensitive to initialisation. The methods that
rely on neural networks for feature ranking are not robust since they can have
variations in their ranking when the model is initialized and trained with
different random seeds. In this work, we introduce a novel method based on
parameter averaging to estimate accurate and robust feature importance in
tabular data setting, referred as XTab. We first initialize and train multiple
instances of a shallow network (referred as local masks) with "different random
seeds" for a downstream task. We then obtain a global mask model by "averaging
the parameters" of local masks. We show that although the parameter averaging
might result in a global model with higher loss, it still leads to the
discovery of the ground-truth feature importance more consistently than an
individual model does. We conduct extensive experiments on a variety of
synthetic and real-world data, demonstrating that the XTab can be used to
obtain the global feature importance that is not sensitive to sub-optimal model
initialisation.
