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Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is cross-validation?
π‘ Hint: Think about why we need to evaluate models differently.
Question 2
Easy
Describe K-Fold cross-validation.
π‘ Hint: How many times are we training with K-Fold?
Practice 4 more questions and get performance evaluation
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the primary purpose of cross-validation?
π‘ Hint: Consider why we need to estimate model performance better.
Question 2
True or False: Leave-One-Out cross-validation is always preferable to K-Fold cross-validation.
π‘ Hint: Consider the trade-offs of computation vs. training accuracy.
Solve and get performance evaluation
Push your limits with challenges.
Question 1
Given a dataset of 10,000 samples with an imbalanced class distribution of 90% to 10%, how would you set up a stratified K-Fold cross-validation?
π‘ Hint: Think about the ratio of classes in each fold.
Question 2
Analyze the potential impact of using plain K-Fold cross-validation versus stratified K-Fold on a highly imbalanced dataset.
π‘ Hint: Consider how the class ratios affect predictive modeling.
Challenge and get performance evaluation