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Machine Learning Quiz Questions
1.
What is a decision boundary?
A. The region of an input space that corresponds to a particular class label
B. The line that separates the positive and negative classes in a binary classification problem
C. The threshold value used to make a binary classification decision
D. The gradient of a model's loss function
view answer:
B. The line that separates the positive and negative classes in a binary classification problem
Explanation:
A decision boundary is the line (or surface) that separates the positive and negative classes in a binary classification problem. It is determined by the model during the training process, and it is used to classify new data points. When a new data point is located on one side of the decision boundary, it is classified as belonging to the positive class, and when it is located on the other side, it is classified as belonging to the negative class. In other words, the decision boundary is used to make decisions about class membership based on the model's learned parameters.
2.
What is a prototype explanation?
A. An explanation of why a model made a certain decision based on a prototype instance that represents a particular class
B. An explanation of why a model made a certain decision based on its internal state
C. An explanation of how a model was trained
D. An explanation of the limitations of a model
view answer:
A. An explanation of why a model made a certain decision based on a prototype instance that represents a particular class
Explanation:
A prototype explanation is an explanation of why a model made a certain decision based on a prototype instance that represents a particular class. It is a type of XAI technique that helps to make the decision-making process more transparent.
3.
What is a surrogate model?
A. A simple, interpretable model that is used to approximate the behavior of a more complex model
B. A model that is trained on a subset of the data to improve its performance
C. A model that is trained on synthetic data to improve its robustness
D. A model that is trained using a generative adversarial network (GAN)
view answer:
A. A simple, interpretable model that is used to approximate the behavior of a more complex model
Explanation:
A surrogate model is a simple, interpretable model that is used to approximate the behavior of a more complex model. It is often used in XAI to provide explanations for black-box models.
4.
What is an anchor explanation?
A. An explanation of why a model made a certain decision based on the anchor points of the input space
B. An explanation of why a model made a certain decision based on its internal state
C. An explanation of how a model was trained
D. An explanation of the limitations of a model
view answer:
A. An explanation of why a model made a certain decision based on the anchor points of the input space
Explanation:
An anchor explanation is an explanation of why a model made a certain decision based on the anchor points of the input space. It is a type of XAI technique that provides a simple and interpretable rule for making predictions.
5.
What is LIME?
A. A technique for visualizing the decision boundaries of a model
B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
C. A technique for reducing the size of a model by removing unimportant features
D. A technique for improving the accuracy of a model by generating synthetic data
view answer:
B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
Explanation:
LIME (Local Interpretable Model-Agnostic Explanations) is a technique for interpreting the predictions of a model by approximating it with a simple, interpretable model.
6.
What is SHAP?
A. A technique for visualizing the decision boundaries of a model
B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
C. A technique for reducing the size of a model by removing unimportant features
D. A technique for improving the accuracy of a model by generating synthetic data
view answer:
B. A technique for interpreting the predictions of a model by approximating it with a simple, interpretable model
Explanation:
SHAP (SHapley Additive exPlanations) is a technique for interpreting the predictions of a model by approximating it with a simple, interpretable model.
7.
What is the purpose of a saliency map?
A. To highlight the important features in an input that contributed to a model's prediction
B. To visualize the decision boundaries of a model
C. To measure the accuracy of a model
D. To generate new training data for a model
view answer:
A. To highlight the important features in an input that contributed to a model's prediction
Explanation:
A saliency map is a type of visualization that highlights the important features in an input that contributed to a model's prediction.
8.
What is a counterfactual explanation?
A. An explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value
B. An explanation of why a model made a certain decision based on its internal state
C. An explanation of how a model was trained
D. An explanation of the limitations of a model
view answer:
A. An explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value
Explanation:
A counterfactual explanation is an explanation of why a model made a certain decision based on the counterfactual scenario of changing a particular feature value. It is a type of "what-if" analysis that helps to make the decision-making process more transparent.
9.
What is a white-box model?
A. A model that is transparent and easily interpretable
B. A model that is completely opaque and difficult to interpret
C. A model that is partially transparent and only reveals certain aspects of its decision-making process
D. A model that is based on quantum mechanics and cannot be understood by humans
view answer:
A. A model that is transparent and easily interpretable
Explanation:
A white-box model is a model that is transparent and easily interpretable. In other words, the internal workings of the model are easily understandable and transparent to humans.
10.
What is a gray-box model?
A. A model that is transparent and easily interpretable
B. A model that is completely opaque and difficult to interpret
C. A model that is partially transparent and only reveals certain aspects of its decision-making process
D. A model that is based on quantum mechanics and cannot be understood by humans
view answer:
C. A model that is partially transparent and only reveals certain aspects of its decision-making process
Explanation:
A gray-box model is a model that is partially transparent and only reveals certain aspects of its decision-making process. It is somewhere between a black-box and a white-box model.
11.
What is model interpretability?
A. The ability of a model to make accurate predictions
B. The ability of a model to explain its predictions and decisions in a way that is understandable to humans
C. The ability of a model to learn from new data
D. The ability of a model to generalize to new situations
view answer:
B. The ability of a model to explain its predictions and decisions in a way that is understandable to humans
Explanation:
Model interpretability refers to the ability of a model to explain its predictions and decisions in a way that is understandable to humans.
12.
What is a decision tree?
A. A type of model that uses a tree-like structure to represent decisions and their consequences
B. A type of model that uses deep learning to make decisions
C. A type of model that is completely opaque and difficult to interpret
D. A type of model that is based on quantum mechanics and cannot be understood by humans
view answer:
A. A type of model that uses a tree-like structure to represent decisions and their consequences
Explanation:
A decision tree is a type of model that uses a tree-like structure to represent decisions and their consequences. It is a white-box model that is often used in XAI.
13.
What is the importance of XAI?
A. It allows humans to better understand and trust the decisions made by AI systems
B. It helps to make AI systems more efficient and accurate
C. It is necessary for AI systems to function at all
D. It allows AI systems to operate autonomously without human intervention
view answer:
A. It allows humans to better understand and trust the decisions made by AI systems
Explanation:
The importance of XAI is that it enables humans to better understand and trust the decisions made by AI systems, which is crucial for their adoption in many applications.
14.
What is a black-box model?
A. A model that is transparent and easily interpretable
B. A model that is completely opaque and difficult to interpret
C. A model that is partially transparent and only reveals certain aspects of its decision-making process
D. A model that is based on quantum mechanics and cannot be understood by humans
view answer:
B. A model that is completely opaque and difficult to interpret
Explanation:
A black-box model is a model that is completely opaque and difficult to interpret. In other words, the internal workings of the model are not easily understandable or transparent to humans.
15.
What is Explainable AI (XAI)?
A. A type of machine learning that is able to explain its predictions and decisions
B. A type of deep learning that is used to generate explanations
C. A type of reinforcement learning that is used to train explainable agents
D. A type of unsupervised learning that is used to discover hidden patterns
view answer:
A. A type of machine learning that is able to explain its predictions and decisions
Explanation:
Explainable AI (XAI) refers to a set of techniques and methods that enable machine learning models to explain their predictions and decisions in a way that is understandable to humans.
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