PROFESSIONAL-MACHINE-LEARNING-ENGINEER · Question #327
You are developing an ML model on Vertex AI that needs to meet specific interpretability requirements for regulatory compliance. You want to use a combination of model architectures and modeling techn
The correct answer is C. Use a boosted decision tree-based model architecture, and use SHAP values for interpretability.. Boosted decision tree models (e.g., XGBoost) offer strong predictive performance while being more interpretable than deep learning models. SHAP values provide consistent, theoretically grounded explanations for model predictions, making this combination well-suited for meeting in
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Options
- AUse a convolutional neural network (CNN)-based deep learning model architecture, and use local
- BUse a recurrent neural network (RNN)-based deep learning model architecture, and use integrated
- CUse a boosted decision tree-based model architecture, and use SHAP values for interpretability.
- DUse a long short-term memory (LSTM)-based model architecture, and use local interpretable
How the community answered
(20 responses)- A5% (1)
- B5% (1)
- C75% (15)
- D15% (3)
Explanation
Boosted decision tree models (e.g., XGBoost) offer strong predictive performance while being more interpretable than deep learning models. SHAP values provide consistent, theoretically grounded explanations for model predictions, making this combination well-suited for meeting interpretability requirements in regulated environments.
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