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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is the purpose of dimensionality reduction?
💡 Hint: Think about the challenges of high-dimensional data.
Question 2
Easy
What does PCA stand for?
💡 Hint: Remember it is a method for linear dimensionality reduction.
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 the main goal of dimensionality reduction?
- To increase the number of features
- To simplify datasets
- To eliminate data
💡 Hint: Remember the outcomes of reducing features.
Question 2
True or False: t-SNE is primarily used for dimensionality reduction of datasets intended for model input.
- True
- False
💡 Hint: Consider the main purpose of t-SNE.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
Given a dataset with 500 features, you want to reduce it down to a more manageable number while retaining as much variance as possible. Explain how you would use PCA for this task, including key steps.
💡 Hint: Start by considering the necessity of standardization and covariance.
Question 2
Consider the case of anomaly detection in a dataset with many varied features. Decide whether you would prefer feature selection or extraction to enhance performance. Justify your answer.
💡 Hint: Think about the nature of the data and the desired outcomes.
Challenge and get performance evaluation