Practice Module Objectives (for Week 8) - 4.1 | Module 4: Advanced Supervised Learning & Evaluation (Weeks 8) | Machine Learning
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4.1 - Module Objectives (for Week 8)

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What does ROC curve measure?

💡 Hint: Think about what is being plotted on the axes of the ROC curve.

Question 2

Easy

Name the two metrics that the Precision-Recall curve measures.

💡 Hint: These metrics relate to the classification of positive instances.

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 the ROC curve represent?

  • A tool for analyzing economic data.
  • A plot of true positive rates against false positive rates.
  • A measure of data central tendency.

💡 Hint: Consider the axis labels.

Question 2

True or False: AUC can be misleading in cases of class imbalances.

  • True
  • False

💡 Hint: Think about the implications of extreme class imbalance.

Solve 2 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

You have developed a model with an AUC of 0.6 on an imbalanced dataset. How do you plan to address the situation?

💡 Hint: Think about what AUC indicates in terms of model capability.

Question 2

Given a validation curve for a hyperparameter, describe how you would identify the optimal value and risk of overfitting.

💡 Hint: Visual aids can help clarify the transition point in your analysis.

Challenge and get performance evaluation