What is PCA?
An example of an AI chatbot conversation about Principal Component Analysis (PCA).
Can you explain what PCA is?
Sure! PCA, or Principal Component Analysis, is a statistical technique used to reduce the dimensionality of data.
How does PCA work?
PCA works by identifying the directions (principal components) in which the data varies the most and projecting the data onto these directions.
What are the applications of PCA?
PCA is commonly used in fields like image processing, genetics, and finance for data visualization and noise reduction.
Are there any limitations to PCA?
Yes, PCA assumes linear relationships and can be sensitive to outliers, which may affect the results.
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