6.2.1 - Why Reduce Dimensions?
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Practice Questions
Test your understanding with targeted questions
What is meant by the curse of dimensionality?
💡 Hint: Think about how data points might spread out.
How does reducing dimensions improve computational cost?
💡 Hint: Consider how many features algorithms need to work with.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is the curse of dimensionality?
💡 Hint: Think about the effect of having too many features.
True or False: Dimensionality reduction can lead to overfitting.
💡 Hint: Consider how reducing features impacts model training.
2 more questions available
Challenge Problems
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Consider a high-dimensional dataset with 100 features. Discuss the potential impact this may have on a machine learning model and how dimensionality reduction could help.
💡 Hint: Think about the relationship between dimensions and data density.
In a practical application, how would you decide which features to retain or discard when reducing dimensions?
💡 Hint: What metrics indicate feature importance?
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