Which approach ranks input features by their influence on the final result, without considering interactions?

Which approach ranks input features by their influence on the final result, without considering interactions?

• A. Counterfactual Explanations
• B. Surrogate Models
• C. Deontology
• D. Feature Importance Scores

Answer: (D)

Feature importance scores measure how much each input feature sways the model’s final prediction, producing a rank of features from most to least influential. This is typically done by perturbing or removing a feature and observing the resulting drop (or change) in model performance; features whose perturbation causes a larger drop are considered more influential. Because this method centers on the individual contribution of each feature, it provides a straightforward ranking of inputs by their impact without explicitly modeling how features interact with one another. Counterfactual explanations, on the other hand, focus on what minimal changes to inputs would flip or move the outcome to a desired target, not on ranking features by overall influence. Surrogate models attempt to approximate the original model with a simpler, more interpretable model and can yield insights about feature effects, but the primary aim isn’t to rank features by influence from the original model alone. Deontology isn’t related to how models attribute importance to features.

Feature importance scores measure how much each input feature sways the model’s final prediction, producing a rank of features from most to least influential. This is typically done by perturbing or removing a feature and observing the resulting drop (or change) in model performance; features whose perturbation causes a larger drop are considered more influential. Because this method centers on the individual contribution of each feature, it provides a straightforward ranking of inputs by their impact without explicitly modeling how features interact with one another.

Counterfactual explanations, on the other hand, focus on what minimal changes to inputs would flip or move the outcome to a desired target, not on ranking features by overall influence. Surrogate models attempt to approximate the original model with a simpler, more interpretable model and can yield insights about feature effects, but the primary aim isn’t to rank features by influence from the original model alone. Deontology isn’t related to how models attribute importance to features.