Match each ML model interpretability method to its description.
Drag a concept onto its matching description — or click a concept then click the description.
Game-theoretic approach to explain feature contributions
Local surrogate model to explain individual predictions
Ranking features by their impact on model output
Shows marginal effect of a feature on predictions
Measures decrease in performance when feature is shuffled
⚠ Common exam trap
The most common trap is confusing local vs global interpretability methods. LIME and SHAP are local, while PDP and Permutation Importance are global.
Answer choices
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.
LIME (Local Interpretable Model-agnostic Explanations) provides local explanations by approximating the model's behavior near a specific prediction using a simpler interpretable model. SHAP (SHapley Additive exPlanations) uses Shapley values from game theory to fairly distribute feature contributions for individual predictions. Partial Dependence Plots (PDP) show the average marginal effect of one or two features on the predicted outcome across the dataset, making it a global method. Permutation Feature Importance measures the increase in prediction error when a feature's values are randomly shuffled, indicating feature importance globally. Common mistakes: confusing LIME with a global method (B) and misattributing SHAP to visualizing decision trees (D).
Answer analysis
For each option: why learners choose it and why it is or isn't the right answer here.
LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.
Why this is correct
LIME (Local Interpretable Model-agnostic Explanations) approximates the model's predictions locally using a simpler interpretable model.
LIME: Global interpretability method that provides feature importance rankings across entire dataset.
Why it's wrong here
This describes permutation feature importance or global methods, not LIME which is local.
SHAP: Shapley additive explanations based on game theory to compute feature contributions.
Why this is correct
SHAP uses Shapley values from cooperative game theory to assign importance to each feature.
SHAP: A method for visualizing individual decision trees.
Why it's wrong here
SHAP is not limited to trees; it is model-agnostic, though TreeSHAP is a variant for tree-based models.
Partial Dependence Plot: Shows average marginal effect of one or two features on predicted outcome.
Why this is correct
Partial dependence plots illustrate how the model's predictions change as one or two features vary.
Permutation Feature Importance: Measures decrease in model performance when feature values are shuffled.
Why this is correct
Permutation importance computes the drop in model score when a feature's values are randomly permuted.
Go deeper
About these practice questions
One of 775 original PMLE practice questions on Courseiva, each with a full explanation and wrong-answer analysis — not exam dumps or protected exam content. Learn why practice questions differ from exam dumps →
Written by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
This PMLE practice question is part of Courseiva's free Google Cloud certification practice question bank. Courseiva provides original exam-style practice questions with explanations, topic-based practice, mock exams, readiness tracking, and study analytics to help learners prepare for the PMLE exam.