Practice Feature Selection vs. Feature Extraction: Strategic Data Reduction - 2.4 | Module 5: Unsupervised Learning & Dimensionality Reduction (Weeks 10) | Machine Learning
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2.4 - Feature Selection vs. Feature Extraction: Strategic Data Reduction

Learning

Practice Questions

Test your understanding with targeted questions related to the topic.

Question 1

Easy

What is the main goal of feature selection?

💡 Hint: Think about why we might want to keep certain features and discard others.

Question 2

Easy

What does feature extraction aim to achieve?

💡 Hint: Consider the idea of creating new combinations from existing features.

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 feature selection primarily concerned with?

  • Transforming existing features
  • Selecting a subset of original features
  • Creating new features from existing ones

💡 Hint: Think about keeping only what is necessary for your analysis.

Question 2

True or False: Feature extraction results in the same feature set as the original data.

  • True
  • False

💡 Hint: Recall the definition of feature extraction.

Solve 3 more questions and get performance evaluation

Challenge Problems

Push your limits with challenges.

Question 1

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.

Question 2

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.

Challenge and get performance evaluation