Investigating "Suspicious" Transactions with PFIExplainer

Answer: B.

Reference:

The PFIExplainer (Permutation Feature Importance Explainer) is a technique used to explain the predictions of machine learning models by determining which input features are the most influential in making the prediction. It works by permuting or shuffling the values of each feature one at a time and measuring the effect on the model's prediction accuracy.

In this scenario, the company is operating a webshop and using a real-time inferencing web service to identify potentially risky transactions. One of the transactions has been classified as “suspicious,” and the task is to investigate which features made the model classify it as such.

Using PFIExplainer can serve this purpose as it will help identify the features that contributed the most to the model's decision to classify the transaction as “suspicious.” By analyzing the permutation feature importance scores, the data scientists can determine which features have the greatest impact on the model's predictions.

However, it is important to note that the PFIExplainer is not a perfect solution and has some limitations. For example, it assumes that each feature is independent of the others, which may not always be the case. Additionally, it may not be able to explain complex interactions between features.

In summary, using PFIExplainer can serve the purpose of identifying which features made the model classify a transaction as “suspicious.” However, data scientists should also be aware of its limitations and use it in conjunction with other techniques to get a more complete understanding of the model's behavior. The answer is A. Yes.