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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What does PCA stand for?
💡 Hint: Think about what 'P' stands for.
Question 2
Easy
What is the first step in PCA?
💡 Hint: Consider what we often do to data before analyzing.
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 purpose of PCA?
- To increase dimensions
- To reduce dimensions while preserving variance
- To eliminate all data
- To classify data into categories
💡 Hint: Remember, PCA simplifies datasets.
Question 2
True or False: PCA requires the data to be normalized before applying the technique.
- True
- False
💡 Hint: Think about the first step in PCA.
Solve 2 more questions and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You have a high-dimensional dataset containing features from several products. Describe how you would apply PCA to simplify your analysis and what insights you hope to gain.
💡 Hint: Think about the goals when simplifying a dataset.
Question 2
Critique the use and limitation of PCA in machine learning, especially regarding its effectiveness with non-linear datasets.
💡 Hint: Consider how PCA aligns with the dataset’s inherent relationships.
Challenge and get performance evaluation