After the transformation, we generate a high-dimensional dataset describing the features of the perturbed and the reference states. These data are characterized by labels, namely the bin number and the perturbation intensity (or the strength of the electric field) (see Table ). For many systems, it becomes extremely useful to project such high-dimensional dataset into a lower dimensional space in such a way that the difference between the dataset associated with the reference state and the perturbative state is maximum. This is precisely the reason for which we employ the Linear Discriminant Analysis (LDA) to the high-dimensional data points of the reference and perturbed states. This ensures that these data points are projected into a lower dimensional space, where the separation between the classes (datapoints of the reference and the perturbed states) is maximum;