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Questions and Answers
PCA is a supervised method.
PCA is a supervised method.
False
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
PCA can be used for visualizing data in higher dimensions.
PCA can be used for visualizing data in higher dimensions.
False
All principal components are orthogonal to each other.
All principal components are orthogonal to each other.
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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.
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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.
