Practice Common Evaluation Metrics - 12.2 | 12. Model Evaluation and Validation | Data Science Advance
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Common Evaluation Metrics

12.2 - Common Evaluation Metrics

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the formula for accuracy?

💡 Hint: Consider the components of true positives and negatives.

Question 2 Easy

Define precision in your own words.

💡 Hint: Think about false positives.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does accuracy measure in classification tasks?

True positive rate
Overall correctness
False positive rate

💡 Hint: Think about all correct and incorrect predictions.

Question 2

Is Precision always more informative than Accuracy in imbalanced datasets?

True
False

💡 Hint: Consider the impact of negative cases.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a class imbalance where you predicted 100 positives and 70 of them are true positives, while 30 are false positives. Calculate precision. Discuss the significance.

💡 Hint: Think about what happens when false positives are high.

Challenge 2 Hard

Given MSE = 16 and N=4 for your predictions, how would you interpret this in terms of model performance? Compare this with RMSE.

💡 Hint: Consider how errors impact decisions.

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