  Gradients can be employed for sensitivity analysis. Here, we leverage the
advantages of the Loss Landscape to comprehend which independent variables
impact the dependent variable. We seek to grasp the loss landscape by utilizing
first, second, and third derivatives through automatic differentiation. we know
that Spearman's rank correlation coefficient can detect the monotonic
relationship between two variables. However, I have found that second-order
gradients, with certain configurations and parameters, provide information that
can be visualized similarly to Spearman results, In this approach, we
incorporate a loss function with an activation function, resulting in a
non-linear pattern. Each exploration of the loss landscape through retraining
yields new valuable information. Furthermore, the first and third derivatives
are also beneficial, as they indicate the extent to which independent variables
influence the dependent variable.
