Practice - Dimensionality Reduction: Principal Component Analysis (PCA) Introduction
Practice Questions
Test your understanding with targeted questions
What is the purpose of dimensionality reduction in machine learning?
💡 Hint: Think about how too many features can confuse a model.
Define Principal Component Analysis (PCA).
💡 Hint: It's about finding directions that explain the most variation.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does PCA stand for?
💡 Hint: It's a key method in dimensionality reduction.
True or False: The first principal component captures the least variance of the data.
💡 Hint: Think about how variance is measured in PCA.
2 more questions available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with 100 features, you perform PCA and decide to keep only the top 10 principal components. Discuss how this can affect your model, both positively and negatively.
💡 Hint: Consider the balance between dimensionality reduction and information retention.
You're tasked with applying PCA to a dataset for a classification problem. Describe how you would approach implementing PCA step-by-step, and what considerations you must take into account regarding data interpretation.
💡 Hint: Think about the sequential approach and what each step entails.
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Reference links
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