MLA-C01 Question 141
Select 3You are designing a machine learning model for a financial institution to predict the likelihood of loan defaults. The institution requires the model's predictions to be interpretable by regulators and non-technical stakeholders. Which of the following considerations would be most important when selecting the model or algorithm?
- A
Choose a simple model such as linear regression or decision trees, as they offer high interpretability.
- B
Use a highly complex deep learning model since it generally provides the most accurate predictions.
- C
Consider using SHAP (SHapley Additive exPlanations) or LIME to interpret the predictions of a more complex model if high accuracy is required.
- D
Focus only on improving the model's accuracy and ignore interpretability, as accuracy is the most critical factor.
- E
Evaluate the tradeoff between accuracy and interpretability based on the institution's regulatory and stakeholder requirements.
Show answer and explanation
Correct answers: A, C, E
Explanation
In scenarios where interpretability is a critical requirement, such as in financial institutions or regulated industries, selecting a model involves balancing accuracy and interpretability. Simple models like linear regression and decision trees are inherently interpretable, making them a good first option. However, if higher accuracy is required, tools like SHAP or LIME can help explain predictions of complex models. Ultimately, the choice should align with the institution's regulatory and stakeholder requirements, ensuring both accuracy and interpretability are adequately addressed.
- A. Correct.
This is correct because simple models like linear regression and decision trees are inherently interpretable, making them suitable for scenarios where stakeholders prioritize interpretability.
- B. Incorrect.
This is incorrect because while deep learning models may offer high accuracy, they are often considered black-box models and are not inherently interpretable, which is a key requirement in this scenario.
- C. Correct.
This is correct because tools like SHAP and LIME can explain predictions from complex models, thus addressing the need for interpretability alongside high accuracy.
- D. Incorrect.
This is incorrect because interpretability is explicitly stated as a requirement in the scenario, so ignoring it would not meet the institution's needs.
- E. Correct.
This is correct because choosing the appropriate model involves carefully balancing accuracy and interpretability based on the institution's specific requirements, especially in regulated industries.