Practice Perform Comprehensive Model Evaluation - 6.6 | Module 3: Supervised Learning - Classification Fundamentals (Weeks 5) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does a confusion matrix display?

πŸ’‘ Hint: Think of how many predictions were right or wrong.

Question 2

Easy

Why is accuracy sometimes a misleading metric?

πŸ’‘ Hint: Consider cases where one outcome is rare.

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 the primary purpose of a confusion matrix?

  • To visualize the performance of regression models
  • To summarize the performance of a classification model
  • To track user engagement

πŸ’‘ Hint: Think about what classifications are being compared.

Question 2

True or False: A high accuracy always indicates a good model performance.

  • True
  • False

πŸ’‘ Hint: Remember cases where accuracy can be misleading.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Given a dataset where the classifier has an accuracy of 90% but precision is 50% and recall is 75%, analyze its performance.

πŸ’‘ Hint: Think about what each metric tells you about user experience.

Question 2

A model is used for fraud detection with a 70% accuracy but has a recall of only 30%. What does this suggest about the model's performance in catching fraud cases?

πŸ’‘ Hint: Consider how much of the target class is being missed by the model.

Challenge and get performance evaluation