Networks of various depths, specifically with 1, 2, and 3 hidden layers, were tested. This is motivated by prior evidence that deep networks can represent certain families of functions more efficiently (more compactly) than shallow networks . In other words, model depth can also contribute to model capacity. Layers with 10, 20, 50, 75, and 100 nodes were constructed, and in deep networks the number of nodes was kept the same for all their layers. FNN models were not trained with feature config 'A' as it captures a very limited feature space that would not justify the model complexity.