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What does a ROC curve of a random classifier look like?
What does a ROC curve of a random classifier look like?
- A line from (0.0, 0.0) to (0.0, 1.0) and further to (1.0, 1.0)
- A straight line from (0.0, 0.0) to (1.0, 1.0) (correct)
- A line from (0.0, 0.0) to (1.0, 0.0)
- A curve that connects multiple points
How do you interpret the ROC curve in the area with the top left corner (0.0, 1.0)?
How do you interpret the ROC curve in the area with the top left corner (0.0, 1.0)?
- Indicates good performance levels (correct)
- Indicates poor performance levels
- Shows no classifier performance
- Is a random classifier
What is the characteristic of a classifier with perfect performance level?
What is the characteristic of a classifier with perfect performance level?
- Combination of two straight lines from (0.0, 0.0) to (0.0, 1.0) and further to (1.0, 1.0) (correct)
- Line from (0.0, 0.0) to (1.0, 1.0)
- It shows random performance
- Curve connecting multiple points
What separates the space into two areas for good and poor performance levels in a ROC curve?
What separates the space into two areas for good and poor performance levels in a ROC curve?
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How does a perfect classifier's ROC curve differ from a random classifier's ROC curve?
How does a perfect classifier's ROC curve differ from a random classifier's ROC curve?
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In the context of ROC curves, what does the AUC score measure?
In the context of ROC curves, what does the AUC score measure?
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What does it mean when a ROC curve lies between the random and perfect ROC curves?
What does it mean when a ROC curve lies between the random and perfect ROC curves?
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How does connecting multiple points help in creating a ROC curve?
How does connecting multiple points help in creating a ROC curve?
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What does the bottom right corner of a ROC curve indicate?
What does the bottom right corner of a ROC curve indicate?
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What is the significance of connecting ROC points to create a curve?
What is the significance of connecting ROC points to create a curve?
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