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What is PCA for Feature Extraction Explained about?

The document discusses feature extraction and principal component analysis (PCA). PCA is an unsupervised feature extraction technique that creates new features from linear combinations of the original features. The new features are orthogonal and ranked by the amount of variance they explain in the dataset. PCA can reduce dimensionality by keeping only the number of principal components needed to explain a certain percentage of variance, such as 90%. PCA requires normalizing the dataset first.

Author

Amandeep Sharma

Language

EN