Professional Machine Learning Engineer Exam - Question 75

Question

You work at a subscription-based company. You have trained an ensemble of trees and neural networks to predict customer churn, which is the likelihood that customers will not renew their yearly subscription. The average prediction is a 15% churn rate, but for a particular customer the model predicts that they are 70% likely to churn. The customer has a product usage history of 30%, is located in New York City, and became a customer in 1997. You need to explain the difference between the actual prediction, a 70% churn rate, and the average prediction. You want to use Vertex Explainable AI. What should you do?

Examice · Accepted Answer

To explain individual predictions for an ensemble of trees and neural networks, configuring sampled Shapley explanations on Vertex Explainable AI is the most appropriate approach. Sampled Shapley values provide a model-agnostic method to attribute the contribution of each feature to the prediction, making it suitable for non-differentiable models such as ensembles and neural networks.

JeanEl · Answer

Agree with B :  individual instance prediction + ensemble of trees and neural networks (recommended model types for Sampled Shapley : "Non-differentiable models, such as ensembles of trees and neural networks " ). Check out the link below :
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

pmle_nintendo · Answer

Sampled Shapley explanations offer a more sophisticated and model-agnostic method for understanding feature importance and contributions to predictions.

YangG · Answer

it is about a individual instance prediction. I think use integrated gradient method

hiromi · Answer

B
- https://christophm.github.io/interpretable-ml-book/shapley.html
- https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

emma_aic · Answer

https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#sampled-shapley

John_Pongthorn · Answer

B is optimal for tabular data Tree or DNN

C integrated gradients explanations on Vertex Explainable AI.
It is used for image.

enghabeth · Answer

Sampled Shapley works well for these models, which are meta-ensembles of trees and neural networks.
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#sampled-shapley

CloudKida · Answer

Assigns credit for the outcome to each feature, and considers different permutations of the features. This method provides a sampling approximation of exact Shapley values.
shampled shapely recommended Model Type: Non-differentiable models, such as ensembles of trees and neural networks.
https://cloud.google.com/ai-platform/prediction/docs/ai-explanations/overview

M25 · Answer

Went with B

NickHapton · Answer

B
refer: 
https://cloud.google.com/vertex-ai/docs/explainable-ai/overview#compare-methods

ares81 · Answer

It seems D.

egdiaa · Answer

B - For sure as per GCP Docs here: https://cloud.google.com/vertex-ai/docs/explainable-ai/overview

ares81 · Answer

It should be B.

adavid213 · Answer

I agree, it seems like B

PhilipKoku · Answer

B) Shapley

Professional Machine Learning Engineer Exam - Question 75

Discussion