Practice Evaluating Model Performance (6.9) - Chapter 6: Supervised Learning

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10 questions

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6 sample questions

Question 1.
Define Mean Squared Error (MSE).
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Think about the errors in predictions.
Question 2.
What does a higher R² Score indicate?
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Consider how it relates to model fit.
3Question 3.
What does a lower Mean Squared Error suggest?
- Worse model fit
- Better model fit
- No effect
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Think about the relationship between error and fit.
4Question 4.
The R² Score can be between which values?
- 0 to 1
- -1 to 1
- Any value
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Consider the meaning of a perfect prediction.
Question 5.
Given a model with predictions [100, 150, 200, 250] and actual values [110, 145, 195, 270], calculate both MSE and R² Score. Discuss the significance of these scores.
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Use the formulas for MSE and R² to find the answers.
Question 6.
You created a linear regression model with a very low R² Score. What steps would you take to investigate potential improvements?
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Think about model assumptions and data quality.

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