Principal Component Analysis
Read OriginalThis article provides a comprehensive tutorial on Principal Component Analysis (PCA), a linear transformation technique for dimensionality reduction. It covers the internals of PCA in three steps: eigendecomposition, selecting principal components, and projection. The guide includes comparisons with LDA, explains the concept of explained variance, and demonstrates implementation using the Iris dataset and scikit-learn.
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