The final SHAP values represent the expected value of gradients * (inputs - baselines). Gradient SHAP adds Gaussian noise to each input sample multiple times, selects a random point along the path between baseline and input, and computes the gradient of outputs with respect to those selected random points. Gradient SHAP is a gradient method to compute SHAP values, which are based on Shapley values proposed in cooperative game theory. To learn more about Integrated Gradients, visit the following resources: More information regarding these axioms can be found in the original paper. The cornerstones of this approach are two fundamental axioms, namely sensitivity and implementation invariance. The equations are copied from the original paper. Integrated Gradients along the i - th dimension of input X. Formally, it can be described as follows: The integral can be approximated using a Riemann Sum or Gauss Legendre quadrature rule. Integrated gradients represents the integral of gradients with respect to inputs along the path from a given baseline to input.
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