Practice Principal Component Analysis (pca) (11.2.1.2) - Representation Learning & Structured Prediction
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Principal Component Analysis (PCA)

Practice - Principal Component Analysis (PCA)

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is the main purpose of PCA?

💡 Hint: Think about how we simplify data without losing significant information.

Question 2 Easy

What do we call the new variables resulting from PCA?

💡 Hint: Remember the role they play in data representation.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the primary goal of PCA?

To increase the dimensions of data
To reduce the dimensions of data
To randomize data

💡 Hint: Think about the overall purpose of data analysis.

Question 2

True or False: PCA can only be used for linear datasets.

True
False

💡 Hint: Consider the types of data PCA works best with.

1 more question available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Given a high-dimensional dataset of images, explain how PCA can assist in visualizing these images more effectively. What considerations must be kept in mind during this transformation?

💡 Hint: Think about what features are most important in an image.

Challenge 2 Hard

Suppose you have applied PCA and retained three principal components. How would you evaluate if these components are adequate for a particular analysis?

💡 Hint: Reflect on how variance retention impacts analysis outcomes.

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Reference links

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