© Ekaba Bisong 2019
E. . BisongBuilding Machine Learning and Deep Learning Models on Google Cloud Platformhttps://doi.org/10.1007/978-1-4842-4470-8_26

26. Principal Component Analysis (PCA)

Ekaba Bisong1 
(1)
OTTAWA, ON, Canada
 

Principal component analysis (PCA) is an essential algorithm in machine learning. It is a mathematical method for evaluating the principal components of a dataset. The principal components are a set of vectors in high-dimensional space that capture the variance (i.e., spread) or variability of the feature space.

The goal of computing principal components is to find a low-dimensional feature sub-space that captures as much information as possible from the original higher-dimensional features of the dataset.

PCA is particularly ...

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