Practice Evaluating Regression Models - 4 | Regression Analysis | Data Science Basic
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4 - Evaluating Regression Models

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Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define Mean Absolute Error and explain its significance in regression.

💡 Hint: Consider how it treats positive and negative errors.

Question 2

Easy

What does a lower RMSE indicate about a regression model?

💡 Hint: Think about the average error of predictions.

Practice 4 more questions and get performance evaluation

Interactive Quizzes

Engage in quick quizzes to reinforce what you've learned and check your comprehension.

Question 1

What does MAE stand for in regression evaluation?

  • Mean Aggregate Error
  • Mean Absolute Error
  • Mean Average Error

💡 Hint: Think about what 'absolute' means in relation to errors.

Question 2

True or False: A higher R² score indicates a better model fit.

  • True
  • False

💡 Hint: Consider what higher values suggest about a model's explanatory power.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

💡 Hint: Break the calculations into steps; calculate absolute errors first, then square them.

Question 2

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?

💡 Hint: Focus on what higher R² scores imply regarding model effectiveness.

Challenge and get performance evaluation