Practice Evaluation Metrics for Deep Learning Models - 8.8 | 8. Deep Learning and Neural Networks | Data Science Advance
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8.8 - Evaluation Metrics for Deep Learning Models

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the formula for accuracy?

💡 Hint: Think about it as a percentage.

Question 2

Easy

Name a metric used for regression tasks.

💡 Hint: Consider metrics looking at errors.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does the F1-score evaluate?

  • Precision only
  • Recall only
  • Balance between Precision and Recall
  • None of the above

💡 Hint: Think of it as a mean of both metrics.

Question 2

True or False: RMSE is achieved by taking the square root of MSE.

  • True
  • False

💡 Hint: Focus on what the formulas indicate.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Use the formulas for Precision, Recall, and then F1-score to compute each.

Question 2

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.

💡 Hint: Focus on understanding what MSE and R² imply for model performance.

Challenge and get performance evaluation