Recall & Review
beginner
What is Principal Component Analysis (PCA)?
PCA is a method to reduce the number of features in data by finding new features called principal components. These components keep most of the important information while making the data simpler.
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beginner
Why do we use PCA in machine learning?
We use PCA to make data easier to work with by reducing its size. This helps models run faster and can improve their accuracy by removing noise and less important details.
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intermediate
What are principal components in PCA?
Principal components are new features created by PCA. Each one is a combination of original features and shows a direction where the data varies the most.
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intermediate
How does PCA find principal components?
PCA finds principal components by calculating directions (called eigenvectors) where data changes the most. It orders these directions by how much data varies along them (eigenvalues).
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beginner
What is the role of variance in PCA?
Variance shows how spread out data is. PCA picks directions with the highest variance because they hold the most important information about the data.
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What does PCA mainly help with in data?
Which of these is a principal component?
What does PCA use to find directions of maximum variance?
Why is variance important in PCA?
What is a common use of PCA in machine learning?
Explain in your own words what Principal Component Analysis (PCA) does and why it is useful.
Describe how PCA finds the directions (principal components) that capture the most variance in data.