MLA-C01 Question 140
Select 3You are developing a machine learning model to predict loan approval for a financial institution. The institution requires that the model's predictions be understandable by non-technical stakeholders, such as loan officers, while still maintaining reasonable accuracy. Which of the following considerations should guide your model or algorithm selection?
- A
Choose a simpler algorithm like logistic regression or decision trees to improve interpretability.
- B
Prioritize the use of ensemble methods like random forests or gradient boosting to maximize accuracy without sacrificing interpretability.
- C
Use SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-Agnostic Explanations) to improve the interpretability of complex models.
- D
Select a deep learning model because it generally provides the best accuracy, regardless of interpretability concerns.
- E
Evaluate the trade-offs between model complexity and interpretability during the experimentation phase.
Show answer and explanation
Correct answers: A, C, E
Explanation
When interpretability is a key requirement, simpler models like logistic regression or decision trees should be considered first. However, if a complex model is needed for better performance, interpretability tools like SHAP or LIME can be used. Additionally, evaluating trade-offs between model complexity, accuracy, and interpretability during experimentation ensures the model aligns with business needs.
- A. Correct.
Simpler algorithms like logistic regression and decision trees are inherently interpretable and are often suitable when interpretability is a key requirement.
- B. Incorrect.
Ensemble methods, while accurate, are complex and harder to interpret, so they are not ideal when interpretability is a priority.
- C. Correct.
Tools like SHAP and LIME can help make complex models interpretable, allowing you to use them in situations where interpretability is required.
- D. Incorrect.
Deep learning models are powerful but are highly complex and generally not suitable for scenarios where interpretability is essential.
- E. Correct.
It is important to evaluate trade-offs between accuracy and interpretability during experimentation to meet business requirements effectively.