Practice - Feature Selection vs. Feature Extraction: Strategic Data Reduction
Practice Questions
Test your understanding with targeted questions
What is the main goal of feature selection?
💡 Hint: Think about why we might want to keep certain features and discard others.
What does feature extraction aim to achieve?
💡 Hint: Consider the idea of creating new combinations from existing features.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What is feature selection primarily concerned with?
💡 Hint: Think about keeping only what is necessary for your analysis.
True or False: Feature extraction results in the same feature set as the original data.
💡 Hint: Recall the definition of feature extraction.
3 more questions available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with 50 features where several are unnecessary or redundant, describe the steps you would take to perform feature selection effectively.
💡 Hint: Consider which methods you think are most efficient and applicable for your analysis.
Suppose you have a dataset of financial transactions with numerous features known to be correlated. How would you apply feature extraction to this dataset, and which method would you choose?
💡 Hint: Think about the steps needed to perform PCA effectively.
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Reference links
Supplementary resources to enhance your learning experience.
- Feature Selection vs Feature Extraction Explained
- Understanding PCA and Dimensionality Reduction
- Feature Selection Techniques in Python
- Feature Extraction Techniques
- Dimensionality Reduction: An Overview
- Feature Selection and Feature Extraction
- What is the Curse of Dimensionality?
- Scikit-learn Documentation on Feature Selection