Practice Step 6: Evaluate the Model - 9.7 | Chapter 9: End-to-End Machine Learning Project – Predicting Student Exam Performance | Machine Learning Basics
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

Define accuracy in the context of model evaluation.

💡 Hint: Think about the total number of cases and correct predictions.

Question 2

Easy

What does precision indicate?

💡 Hint: Focus on true positives versus predicted positives.

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 is a confusion matrix used for?

  • To count incorrect predictions
  • To assess model performance
  • To display training data

💡 Hint: Think about where this matrix is applied.

Question 2

True or False: High accuracy always indicates a good model.

  • True
  • False

💡 Hint: Consider cases where one outcome is much more frequent than the other.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a confusion matrix with TP = 30, TN = 50, FP = 10, FN = 5, calculate the accuracy, precision, recall, and F1 score.

💡 Hint: Use the respective formulas for each metric.

Question 2

Discuss the implications of a model with high accuracy but very low precision. What corrective actions might you take?

💡 Hint: Consider the balance between finding positives and the risk of false positives.

Challenge and get performance evaluation