12.4 - Cross-Validation
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Practice Questions
Test your understanding with targeted questions
What is Cross-Validation?
💡 Hint: Think about how we ensure a model works well on new data.
How does K-Fold Cross-Validation work?
💡 Hint: Remember the process involves repeating training and testing.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the primary goal of Cross-Validation?
💡 Hint: Consider what it means to validate.
True or False: K-Fold Cross-Validation requires splitting data into fixed segments and never rotates those segments.
💡 Hint: Think about the definition of K-Fold.
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Challenge Problems
Push your limits with advanced challenges
Design a Cross-Validation strategy for a dataset with 1000 samples. Explain how you would choose the number of folds (K) and why.
💡 Hint: Think about the trade-offs between computational load and data representation.
Given a model that performs at 80% accuracy on K-Fold Cross-Validation but only 50% on a split dataset, analyze the potential issues.
💡 Hint: Consider the implications of your findings on model quality.
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