Practice Evaluation Metrics For Deep Learning Models (8.8) - Deep Learning and Neural Networks

Practice: Evaluation Metrics for Deep Learning Models

Lesson 8.8Deep Learning and Neural Networks

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10 questions

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4 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
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is the formula for accuracy?
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Think about it as a percentage.
Question 2.
Name a metric used for regression tasks.
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Consider metrics looking at errors.
3Question 3.
What does the F1-score evaluate?
- Precision only
- Recall only
- Balance between Precision and Recall
- None of the above
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Think of it as a mean of both metrics.
4Question 4.
True or False: RMSE is achieved by taking the square root of MSE.
- True
- False
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Focus on what the formulas indicate.
Question 5.
You have a dataset with 100 samples with 45 True Positives, 10 False Positives, 15 False Negatives, and 30 True Negatives. Calculate the Precision, Recall, and F1-score.
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Use the formulas for Precision, Recall, and then F1-score to compute each.
Question 6.
A regression model yields an MSE of 16 and a baseline model has an R² score of 0.6. Interpret these results and suggest improvements.
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Focus on understanding what MSE and R² imply for model performance.

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