- A
LIME: Local interpretable model-agnostic explanations by approximating locally with a simpler model.
LIME (Local Interpretable Model-agnostic Explanations) approximates the model's predictions locally using a simpler interpretable model.
- B
LIME: Global interpretability method that provides feature importance rankings across entire dataset.
Why wrong: This describes permutation feature importance or global methods, not LIME which is local.
- C
SHAP: Shapley additive explanations based on game theory to compute feature contributions.
SHAP uses Shapley values from cooperative game theory to assign importance to each feature.
- D
SHAP: A method for visualizing individual decision trees.
Why wrong: SHAP is not limited to trees; it is model-agnostic, though TreeSHAP is a variant for tree-based models.
- E
Partial Dependence Plot: Shows average marginal effect of one or two features on predicted outcome.
Partial dependence plots illustrate how the model's predictions change as one or two features vary.
- F
Permutation Feature Importance: Measures decrease in model performance when feature values are shuffled.
Permutation importance computes the drop in model score when a feature's values are randomly permuted.
ML Model Interpretability Methods — Matching Quiz
This PMLE practice question tests your understanding of local interpretability. Examine the command output carefully: the correct answer depends on what the output actually shows, not on general recall alone. A key principle to apply: local Interpretability. Once you have made your selection, read the full explanation to reinforce the concept and understand why each distractor is designed to mislead on exam day.
Match each ML model interpretability method to its description.
Answer choices
Why each option matters
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).
Key principle: Local Interpretability
Answer analysis
Option-by-option breakdown
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.
Related concept
Local Interpretability
- ✗
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.
Related concept
Local Interpretability
- ✗
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.
Related concept
Local Interpretability
- ✓
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.
Related concept
Local Interpretability
Common exam traps
Common exam trap: answer the scenario, not the keyword
The most common trap is confusing local vs global interpretability methods. LIME and SHAP are local, while PDP and Permutation Importance are global.
Detailed technical explanation
How to think about this question
Treat this as a scenario question. Identify the problem, the constraint, and the best action. Then compare each option against those facts.
KKey Concepts to Remember
- Local Interpretability
- Global Interpretability
- LIME
- SHAP
TExam Day Tips
- Watch for words such as best, first, most likely and least administrative effort.
- Review why wrong options are wrong, not only why the correct option is correct.
Key takeaway
Local Interpretability
Real-world example
How this comes up in practice
An e-commerce site experiences heavy traffic on Black Friday and near-zero traffic during off-peak weeks. Rather than provisioning permanent large VMs, the team uses auto-scaling groups that add capacity automatically under load and reduce it overnight. Questions like this test whether you understand elasticity, availability zones, and cloud compute scaling patterns.
What to study next
Got this wrong? Here's your next step.
Review local Interpretability, then practise related PMLE questions on the same topic to reinforce the concept.
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FAQ
Questions learners often ask
What does this PMLE question test?
Local Interpretability
What is the correct answer to this question?
The correct answer is: 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).
What should I do if I get this PMLE question wrong?
Review local Interpretability, then practise related PMLE questions on the same topic to reinforce the concept.
What is the key concept behind this question?
Local Interpretability
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Last reviewed: Jun 11, 2026
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