Practice Metrics (4.2) - Classification Algorithms - Data Science Basic

Practice: Metrics

Lesson 4.2Classification Algorithms

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8 questions

Flashcard drill
3 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
2 questions · about 1 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What does TP stand for in the context of a confusion matrix?
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Think about how many times we correctly predicted the positive class.
Question 2.
How would you calculate accuracy from a confusion matrix?
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Remember to add true positives and true negatives.
3Question 3.
What does a True Positive indicate?
- The model predicted a negative case correctly
- The model predicted a positive case correctly
- The model predicted all cases as positive
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Think about what the 'True' refers to in this context.
4Question 4.
Is a low F1-score an indication of poor model performance?
- True
- False
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Consider the relationship between precision and recall.
Question 5.
You have a dataset where 100 instances were tested. The confusion matrix shows TP=70, FP=10, TN=15, FN=5. What are the accuracy, precision, recall, and F1-score?
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Break down each metric using the confusion matrix values.
Question 6.
Propose a strategy to increase the precision of a model that has low precision currently while ensuring not to severely sacrifice recall.
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Think about what kind of adjustments help in precision without completely missing too many actual positive cases.

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