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Knowledge
Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does ROC stand for, and why is it important?
💡 Hint: Consider the relationship between true positive and false positive rates.
Question 2
Easy
Define hyperparameter tuning.
💡 Hint: Think about how these parameters differ from model parameters.
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 the ROC curve?
- To evaluate model accuracy
- To measure false positives
- To illustrate true positive rate vs false positive rate
💡 Hint: Think about what the axes of the ROC curve represent.
Question 2
True or False: Hyperparameters can be learned directly from the training data during the model training phase.
- True
- False
💡 Hint: Consider how they differ from model parameters.
Solve and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are given a dataset with heavy class imbalance. Describe how you would approach model selection and evaluation.
💡 Hint: Think about what metrics give a true picture in imbalance scenarios.
Question 2
Explain how you would document your end-to-end workflow after finishing your mini-project.
💡 Hint: Refer back to the key segments of your project to include in the documentation.
Challenge and get performance evaluation