After being subjected to the LDA, corresponding to each bin, the datapoints appear as separated clusters (one cluster representing the datapoints corresponding to the reference state or the class 𝒚 ! and the other cluster representing the datapoints corresponding to the perturbed state or the class 𝒚 " ) on a 1D line. Application of the LDA ensures that we now have projected the data onto the lower dimensional space in the axis of maximum separability: such dataset becomes easier to analyze. Of course, now that we have projected the data into the lower dimensional space (1 D line), the features lose their physical meaning, given that the new axis is a linear combination of multiple features (or the averages of nx-ys in the given bin for the reference and the perturbed states) and doesn't have any inherent physical meaning. The new axis provides us with the weights of the features. These weights are nothing but the components of the calculate w matrix (which is a 1 × 6 matrix). The six components of the 1 × 6 w matrix are the weight functions of six features. Thus, writing the w matrix as below [see eq.( )]