Practice Metric Description - 4.1 | Regression Analysis | Data Science Basic
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Metric Description

4.1 - Metric Description

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Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does MAE stand for?

💡 Hint: Think about how we measure the average deviation of predictions.

Question 2 Easy

Which metric squares the errors before averaging?

💡 Hint: This metric gives higher penalties for larger errors.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does R² Score indicate in regression analysis?

The average of errors
Proportion of variance explained
Sum of squared errors

💡 Hint: Think about how well the inputs account for outcomes.

Question 2

True or False: RMSE is always less than MSE.

True
False

💡 Hint: Consider the implications of square roots.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a regression model resulting in a high MAE and a low R² score. What steps would you take to improve this model?

💡 Hint: Think about the quality of model fit and data used.

Challenge 2 Hard

Given a dataset with known values and a predicted error of 10, how would one interpret an RMSE of 8?

💡 Hint: Consider how RMSE reflects average prediction error.

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