Practice Evaluation Metrics - 3.3 | Module 2: Supervised Learning - Regression & Regularization (Weeks 3) | Machine Learning
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3.3 - Evaluation Metrics

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does MSE measure?

πŸ’‘ Hint: Think about how the errors are treated in this metric.

Question 2

Easy

True or False: RMSE is the square root of MSE.

πŸ’‘ Hint: Remember the relationship between these two metrics.

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 MSE stand for?

  • Mean Squared Error
  • Mean Standard Error
  • Mean Scaled Error

πŸ’‘ Hint: Remember the emphasis on squaring errors.

Question 2

Is RMSE sensitive to outliers?

  • True
  • False

πŸ’‘ Hint: Consider how squaring impacts larger values.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You predict a student’s test scores based on study hours. The actual scores are [75, 85, 90] for predictions of [70, 88, 91]. Calculate MSE, RMSE, and MAE.

πŸ’‘ Hint: Apply the respective formulas for MSE, RMSE, and MAE.

Question 2

A regression model for predicting sales has an RΒ² of 0.5. Discuss what this might imply about the model and its predictors.

πŸ’‘ Hint: Consider what the percentage tells you about the predictive power of the model.

Challenge and get performance evaluation