AIF-C01 Question 187
Single answerA healthcare organization is building a machine learning model to predict the likelihood of patients developing a certain disease. Due to regulatory requirements, the organization needs to ensure that the model's predictions can be easily interpreted and the decision-making process is explainable to doctors. Which type of model would be the most appropriate for this use case?
- A
A deep learning model with multiple hidden layers
- B
A decision tree-based model
- C
A support vector machine with a radial basis function (RBF) kernel
- D
An ensemble model like a random forest
Show answer and explanation
Correct answer: B
Explanation
For use cases requiring transparency and explainability, models like decision trees are ideal because they provide a clear and intuitive representation of how decisions are made. Other models, such as deep learning or support vector machines, are more complex and lack the inherent simplicity and interpretability needed for regulatory or critical decision-making scenarios like those in healthcare.
- A. Incorrect.
Deep learning models, such as those with multiple hidden layers, are often considered 'black box' models due to their complexity and lack of transparency, making them unsuitable for scenarios requiring explainability.
- B. Correct.
Decision tree-based models are inherently transparent and explainable, as they allow users to clearly trace the decision-making process from input features to predictions. This makes them suitable for scenarios where interpretability is required.
- C. Incorrect.
Support vector machines with a radial basis function (RBF) kernel are complex and not inherently interpretable, as their decision boundaries and weights are not easily understood by humans.
- D. Incorrect.
Ensemble models like random forests, while powerful, lose some degree of transparency and explainability due to their combination of multiple decision trees and aggregated outputs, making them less suitable for scenarios requiring high interpretability.