Practice True Negative (tn) (29.2.2) - Model Evaluation Terminology - CBSE 10 AI (Artificial Intelleigence)

Practice: True Negative (TN)

Lesson 29.2.2Model Evaluation Terminology

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9 questions

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2 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does True Negative (TN) refer to in machine learning?
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Think about what it means to be 'correct' in 'predicted NO'.
Question 2.
Provide an example of a True Negative?
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Consider a context where you check for a specific condition.
3Question 3.
What does True Negative (TN) indicate in model evaluation?
- Correct predictions of negative cases
- Incorrect predictions of positive cases
- Correct predictions of positive cases
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Remember the definition factors in actual outcomes.
4Question 4.
In a confusion matrix, where do we find True Negatives?
- Top right
- Bottom left
- Bottom right
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Visualize the layout of the confusion matrix you're familiar with.
Question 5.
Suppose a model is used for diagnosing a disease with the following confusion matrix: TP = 50, TN = 30, FP = 5, FN = 15. Determine the model's accuracy and explain its implications.
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Use the accuracy formula and remember what the elements represent.
Question 6.
Discuss a scenario where a low TN could lead to negative consequences in healthcare, detailing potential outcomes.
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Think about the consequences of incorrect medical diagnoses.

5 more questions in this section. Enroll free to practise them all.