Practice Evaluating Regression Models (4) - Regression Analysis - Data Science Basic

Practice: Evaluating Regression Models

Lesson 4Regression Analysis

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
Define Mean Absolute Error and explain its significance in regression.
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Consider how it treats positive and negative errors.
Question 2.
What does a lower RMSE indicate about a regression model?
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Think about the average error of predictions.
3Question 3.
What does MAE stand for in regression evaluation?
- Mean Aggregate Error
- Mean Absolute Error
- Mean Average Error
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Think about what 'absolute' means in relation to errors.
4Question 4.
True or False: A higher R² score indicates a better model fit.
- True
- False
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Consider what higher values suggest about a model's explanatory power.
Question 5.
A regression model predicts home prices based on area. Actual prices are $300k, $450k, and $500k, while predicted values are $320k, $430k, and $480k. Calculate the MAE, MSE, and RMSE.
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Break the calculations into steps; calculate absolute errors first, then square them.
Question 6.
You have two models with R² scores: Model A (0.85) and Model B (0.60). What can you infer about the models' performances in explaining variance?
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Focus on what higher R² scores imply regarding model effectiveness.

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