Explainable AI (XAI) Basics

Quiz: Core XAI Concepts and Methods

1. Which of the following best describes the difference between model-specific and model-agnostic methods in explainable AI?

2. Which of the following techniques provides feature attribution in explainable AI?

3. What is a counterfactual explanation in the context of explainable AI?

4. Which method is best suited for explaining any type of machine learning model, regardless of its architecture?

Which of the following best describes the difference between model-specific and model-agnostic methods in explainable AI?

Select the correct answer

Model-agnostic methods require access to model internals, while model-specific methods do not.

Model-specific methods are tailored to particular model architectures, while model-agnostic methods can be applied to any model type.

Model-agnostic methods are only applicable to neural networks.

Model-specific methods are always more accurate than model-agnostic methods.

Which of the following techniques provides feature attribution in explainable AI?

Select the correct answer

Dimensionality reduction

Counterfactual explanations

SHAP

Data augmentation

What is a counterfactual explanation in the context of explainable AI?

Select the correct answer

A visualization of model structure.

An explanation that shows how changing input features would change the model's prediction.

A method for detecting data drift.

A summary of the most important features in a dataset.

Which method is best suited for explaining any type of machine learning model, regardless of its architecture?

Select the correct answer

Decision tree rules

Neural network visualization

Model-agnostic methods

Model-specific methods

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5

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Quiz: Core XAI Concepts and Methods

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1. Which of the following best describes the difference between model-specific and model-agnostic methods in explainable AI?

2. Which of the following techniques provides feature attribution in explainable AI?

3. What is a counterfactual explanation in the context of explainable AI?

4. Which method is best suited for explaining any type of machine learning model, regardless of its architecture?

Which of the following best describes the difference between model-specific and model-agnostic methods in explainable AI?

Select the correct answer

Model-agnostic methods require access to model internals, while model-specific methods do not.

Model-specific methods are tailored to particular model architectures, while model-agnostic methods can be applied to any model type.

Model-agnostic methods are only applicable to neural networks.

Model-specific methods are always more accurate than model-agnostic methods.

Which of the following techniques provides feature attribution in explainable AI?

Select the correct answer

Dimensionality reduction

Counterfactual explanations

SHAP

Data augmentation

What is a counterfactual explanation in the context of explainable AI?

Select the correct answer

A visualization of model structure.

An explanation that shows how changing input features would change the model's prediction.

A method for detecting data drift.

A summary of the most important features in a dataset.

Which method is best suited for explaining any type of machine learning model, regardless of its architecture?

Select the correct answer

Decision tree rules

Neural network visualization

Model-agnostic methods

Model-specific methods

Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 2. Chapter 5