MLA-C01 Question 143
Select 2You are working on a machine learning project to predict whether a patient has a particular disease based on several medical features. The healthcare team has emphasized the importance of understanding how the model makes its predictions to ensure trust and compliance with medical regulations. Which of the following approaches should you consider during model or algorithm selection to prioritize interpretability?
- A
Choose a decision tree-based model such as Random Forest or Gradient Boosted Trees for their interpretability.
- B
Select a linear model, such as logistic regression, as it provides clear insights into feature importance through coefficients.
- C
Opt for a deep learning model, such as a neural network, as it typically provides the best accuracy, even if it is less interpretable.
- D
Consider using SHAP (SHapley Additive exPlanations) or LIME (Local Interpretable Model-agnostic Explanations) with complex models to enhance interpretability.
- E
Leverage unsupervised learning algorithms like k-means clustering for their inherent interpretability.
Show answer and explanation
Correct answers: B, D
Explanation
When interpretability is a key requirement, such as in healthcare applications, selecting inherently interpretable models like logistic regression or using tools like SHAP or LIME to explain predictions from more complex models is crucial. Linear models provide direct insights into feature importance, while SHAP and LIME enhance the interpretability of less transparent algorithms. Decision tree-based models and unsupervised learning methods, while useful in some cases, may not meet the interpretability needs for high-stakes decision-making.
- A. Incorrect.
Decision tree-based models like Random Forest or Gradient Boosted Trees can provide some interpretability, but they are not inherently easy to interpret due to their complexity, especially for ensembles. Feature importance from these models is not always straightforward to understand.
- B. Correct.
Linear models such as logistic regression are inherently interpretable because the feature coefficients directly indicate the relationship between features and the target variable. This makes them a good choice when interpretability is important.
- C. Incorrect.
Deep learning models, such as neural networks, often achieve high accuracy but are considered black-box models due to their complexity. They are not suitable when interpretability is a priority.
- D. Correct.
SHAP and LIME are techniques that can be applied to complex models, such as ensembles or neural networks, to improve interpretability by explaining individual predictions. These tools help bridge the gap between accuracy and interpretability.
- E. Incorrect.
Unsupervised learning algorithms like k-means clustering are not inherently interpretable in the context of feature importance or prediction reasoning. They group data but do not explain relationships or provide explicit reasoning for predictions.