Model Interpretability and Explainability Quiz

1. In the context of model explainability, what does 'model-agnostic' mean?

The method works with any type of model

The method only works with neural networks

The method requires knowing the model's internal parameters

The method is specific to regression problems

2. True or False: A model's high accuracy guarantees that its predictions are easily interpretable.

True

False

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4. In interpretable machine learning, what does 'local' explanation refer to?

Explanations that apply to the entire model globally

Explanations specific to a single prediction or small dataset region

Explanations that only use geographic data

Explanations that require domain expertise

5. True or False: Surrogate models are simpler models trained to approximate a complex model's behavior.

True

False

6. What is the primary limitation of using feature importance alone for model explanation?

It only works for linear models

It doesn't capture feature interactions or individual prediction logic

It increases model training time

It requires labeled test data

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8. Which metric measures how much a feature's removal degrades model performance?

Precision

Permutation Importance

Recall

F1-score

9. True or False: Anchor explanations identify a small set of features that strongly support a prediction.

True

False

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11. What is the primary goal of model interpretability in machine learning?

To increase model accuracy and training speed

To understand why a model makes specific predictions

To reduce the number of features in a dataset

To automate the feature engineering process

12. Counterfactual explanations answer which type of question about model predictions?

What features are most important?

What minimal changes would alter the prediction?

How accurate is the model?

What is the training data distribution?

13. What is the main advantage of using saliency maps in deep learning interpretability?

They reduce model training time significantly

They visualize which input regions most influence the model's prediction

They automatically improve model accuracy

They eliminate the need for validation datasets

14. True or False: Explainability and interpretability are the same concept in machine learning.

True

False

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16. Which of the following is a model-agnostic interpretability method?

Coefficients in linear regression

Feature importance in decision trees

SHAP values

Weights in neural networks

17. What does a partial dependence plot (PDP) show?

The relationship between a feature and the model's prediction, averaged over other features

The correlation matrix of all input features

The distribution of errors in the training dataset

The computational cost of feature extraction

18. True or False: SHAP values are based on game theory and provide a fair allocation of feature contributions.

True

False

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20. Which technique measures the average change in model output when a feature value is altered?

SHAP values

Partial Dependence Plots

Permutation Feature Importance

Local Interpretable Model-agnostic Explanations

DataAI Model Interpretability and Explainability Quiz

1. In the context of model explainability, what does 'model-agnostic' mean?

2.

What first name or nickname would you like us to use?

2. True or False: A model's high accuracy guarantees that its predictions are easily interpretable.

3. SHAP values are derived from the ______ theorem in cooperative game theory.

4. In interpretable machine learning, what does 'local' explanation refer to?

5. True or False: Surrogate models are simpler models trained to approximate a complex model's behavior.

6. What is the primary limitation of using feature importance alone for model explanation?

7. Attention mechanisms in neural networks contribute to model interpretability by showing which ______ the model focuses on.

8. Which metric measures how much a feature's removal degrades model performance?

9. True or False: Anchor explanations identify a small set of features that strongly support a prediction.

10. The concept of 'fairness' in AI explainability refers to ensuring that model explanations are ______ and unbiased.

11. What is the primary goal of model interpretability in machine learning?

12. Counterfactual explanations answer which type of question about model predictions?

13. What is the main advantage of using saliency maps in deep learning interpretability?

14. True or False: Explainability and interpretability are the same concept in machine learning.

15. Feature importance in tree-based models is typically calculated using ______ reduction.

16. Which of the following is a model-agnostic interpretability method?

17. What does a partial dependence plot (PDP) show?

18. True or False: SHAP values are based on game theory and provide a fair allocation of feature contributions.

19. LIME (Local Interpretable Model-agnostic Explanations) works by creating a ______ model around a specific prediction.

20. Which technique measures the average change in model output when a feature value is altered?