Option A is incorrect because training local surrogate models to explain individual predictions is not a

feature of Vertex Explainable AI, but rather a general technique for interpreting black-box

models. Local surrogate models are simpler models that approximate the behavior of the original

model around a specific input1.

Option B is correct because configuring sampled Shapley explanations on Vertex Explainable AI is a

way to explain the difference between the actual prediction and the average prediction for a given

input. Sampled Shapley explanations are based on the Shapley value, which is a game-theoretic

concept that measures how much each feature contributes to the prediction2. Vertex Explainable AI

supports sampled Shapley explanations for tabular data, such as customer churn3.

Option C is incorrect because configuring integrated gradients explanations on Vertex Explainable AI

is not suitable for explaining the difference between the actual prediction and the average prediction

for a given input. Integrated gradients explanations are based on the idea of computing the gradients

of the prediction with respect to the input features along a path from a baseline input to the actual

input4. Vertex Explainable AI supports integrated gradients explanations for image and text data, but

not for tabular data3.

Option D is incorrect because measuring the effect of each feature as the weight of the feature

multiplied by the feature value is not a valid way to explain the difference between the actual

prediction and the average prediction for a given input. This method assumes that the model is linear

and additive, which is not the case for an ensemble of trees and neural networks. Moreover, this

method does not account for the interactions between features or the non-linearity of the model5.

Reference:

Local surrogate models

Shapley value

Vertex Explainable AI overview

Integrated gradients

Feature importance