Practice Model Evaluation Metrics - 6 | Introduction to Machine Learning | Data Science Basic
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Model Evaluation Metrics

6 - Model Evaluation Metrics

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does Mean Squared Error measure?

💡 Hint: Think about errors in predictions.

Question 2 Easy

Define Accuracy in classification tasks.

💡 Hint: Consider what it means to be correct in predictions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the purpose of Mean Squared Error?

To measure prediction accuracy
To quantify prediction interval
To optimize model complexity

💡 Hint: Think about what you're measuring in terms of prediction errors.

Question 2

True or False: A higher R² Score indicates a worse model.

True
False

💡 Hint: Consider what R² measures in relation to variance.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a dataset with imbalanced classes, analyze why relying solely on accuracy could mislead stakeholders in a fraud detection application.

💡 Hint: Consider the implications of fraud being rare.

Challenge 2 Hard

Create a summary report comparing Precision and Recall by using an example from medical diagnosis. Discuss when one may be prioritized over the other.

💡 Hint: Think about the consequences of false negatives versus false positives in health outcomes.

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