What are eigenvalues in PCA and why are they important?
An example of an AI chatbot conversation about understanding eigenvalues in PCA.
What are eigenvalues in PCA and why are they important?
Eigenvalues in PCA represent the amount of variance captured by each principal component. They help determine the significance of each component.
How do you calculate eigenvalues in PCA?
Eigenvalues are calculated from the covariance matrix of the data. You find the eigenvalues by solving the characteristic equation.
Can you explain the relationship between eigenvalues and dimensionality reduction?
Sure! Higher eigenvalues indicate more variance, allowing us to retain significant components while reducing dimensions.
What happens if I ignore the eigenvalues?
Ignoring eigenvalues can lead to retaining less informative components, which may result in loss of important data patterns.
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