Podcast
Questions and Answers
PCA is a supervised method.
PCA is a supervised method.
False (B)
PCA searches for the directions with the smallest variance in the data.
PCA searches for the directions with the smallest variance in the data.
False (B)
PCA can be used for visualizing data in higher dimensions.
PCA can be used for visualizing data in higher dimensions.
False (B)
All principal components are orthogonal to each other.
All principal components are orthogonal to each other.
The maximum number of principal components is greater than the number of features.
The maximum number of principal components is greater than the number of features.
Flashcards
PCA supervised?
PCA supervised?
PCA is an unsupervised method. It does not require labelled data.
PCA minimizes variance?
PCA minimizes variance?
PCA maximizes variance. It finds directions with the highest variance in the data.
PCA for visualizing high-dimensions?
PCA for visualizing high-dimensions?
PCA can be used to visualize high-dimensional data in lower dimensions.
Principal components orthogonal?
Principal components orthogonal?
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Principal components vs features?
Principal components vs features?
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Study Notes
PCA Characteristics
- PCA is an unsupervised method, not a supervised method.
- PCA's goal is to find the directions of highest variance in the data.
- PCA is useful for visualizing high-dimensional data in lower dimensions.
- Principal components (PCs) are orthogonal to each other, meaning they are at right angles and have no correlation.
- The maximum number of principal components is limited by the number of features in the data, and cannot exceed it.
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Description
Test your knowledge about Principle Component Analysis (PCA) with this quiz. Check if statements about PCA are true or false and enhance your understanding of this dimensionality reduction technique.
