Mimic Explainer - Understanding Model Classification Decisions | Webshop Transaction Investigation | DP-100 Exam Prep

Understanding Model Classification Decisions for Suspicious Transactions

Question

You are working for a company which is operating a webshop.

All the transactions flowing through the site are directed to a real-time inferencing web service to identify potentially risky transactions.

One of the transactions is classified by the model as “suspicious” and, before taking actions, you are tasked to investigate which features made the model “think” so.

You decide to use Mimic Explainer to help you understand why this specific transaction has been classified as “suspicious”

Does it serve your purpose?

Answers

Explanations

Click on the arrows to vote for the correct answer

A. B.

Answer: A.

Option A is CORRECT becauseAzure offers a selection of model explainers: Tabular, Mimic and Permutation Feature Importance.

All of them can be used for explaining global importance of features, but only two of them (Tabular, Mimic) are applicable if you need to interpret local importance.

Mimic is a good choice for your task.

Option B is incorrect because either Mimic or Tabular explainer can be used for interpreting the local importance of features, i.e.

Mimic is a good choice.

Reference:

Answer: A. Yes.

Explanation:

Mimic Explainer is a tool that can be used to explain the reasoning behind a model's decision by creating an interpretable and simpler surrogate model that mimics the original model's behavior. The Mimic Explainer generates explanations that can be used to understand how the original model made its decision by considering the input data's features and the model's behavior.

In the given scenario, the objective is to investigate which features caused the model to classify a specific transaction as "suspicious." Mimic Explainer can help achieve this by generating a surrogate model that mimics the behavior of the original model and providing an explanation of the surrogate model's decision-making process. This explanation can help identify which features contributed the most to the model's decision.

Therefore, using Mimic Explainer would serve the purpose of investigating the features that made the model classify the transaction as "suspicious."