Practice Week 8: Advanced Model Evaluation & Hyperparameter Tuning (4.2) - Advanced Supervised Learning & Evaluation (Weeks 8)
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Week 8: Advanced Model Evaluation & Hyperparameter Tuning

Practice - Week 8: Advanced Model Evaluation & Hyperparameter Tuning

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What does the ROC curve plot?

💡 Hint: Remember, TPR is also known as recall.

Question 2 Easy

What is AUC?

💡 Hint: Think about how AUC relates to ROC curves.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

Which curve is better for evaluating imbalanced datasets?

ROC Curve
Precision-Recall Curve
None

💡 Hint: Recall which class is more important in imbalanced data.

Question 2

True or False: AUC of 0.7 indicates a good classifier.

True
False

💡 Hint: Think about the benchmarks for AUC values.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Explain why the Precision-Recall curve is often preferred over the ROC curve when evaluating classifiers on imbalanced datasets.

💡 Hint: Think about how true negatives can affect the representation in traditional metrics.

Challenge 2 Hard

You have a dataset with a substantial imbalance between classes. Discuss the implications of hyperparameter tuning using Random Search versus Grid Search in this context.

💡 Hint: Consider the computational expense of exploring all parameter combinations when you suspect some have more impact.

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Reference links

Supplementary resources to enhance your learning experience.