Practice Anomaly Detection: Identifying the Unusual - 2.2 | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10) | Machine Learning
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Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main purpose of anomaly detection?

πŸ’‘ Hint: Think about unusual occurrences in datasets.

Question 2

Easy

Name one key algorithm used in anomaly detection.

πŸ’‘ Hint: Consider algorithms that specifically focus on outliers.

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 main focus of anomaly detection?

  • To identify rare items or events.
  • To cluster similar data points.
  • To classify labeled data.

πŸ’‘ Hint: Think about what makes 'anomaly' different.

Question 2

Isolation Forest explicitly isolates anomalies. True or False?

  • True
  • False

πŸ’‘ Hint: Remember its core principle of isolation.

Solve 1 more question and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

Imagine you have a dataset of network traffic. How would you approach detecting anomalies using both Isolation Forest and One-Class SVM? Describe the steps and rationale behind your choices.

πŸ’‘ Hint: Think about how layered detection enhances accuracy.

Question 2

Design a simple anomaly detection system for a medical patient monitoring system. What algorithms would you use and why?

πŸ’‘ Hint: Consider real-time monitoring aspects and data dimensionality in your design.

Challenge and get performance evaluation