Data Science Solutions with Python: Fast and Scalable Models Using Keras, PySpark MLlib, H2O, XGBoost, and Scikit-Learn

© The Author(s), under exclusive license to APress Media, LLC, part of Springer Nature 2022

T. C. NokeriData Science Solutions with Pythonhttps://doi.org/10.1007/978-1-4842-7762-1_9

9. Principal Component Analysis with Scikit-Learn, PySpark, and H2O

Tshepo Chris Nokeri¹  

(1)

Pretoria, South Africa

 

This chapter executes a simple dimension reducer (a principal component method) by implementing a diverse set of Python frameworks (Scikit-Learn, PySpark, and H2O). To begin, it clarifies how the method computes components.

Exploring the Principal Component Method

The principal component method is a simple dimension reducer. It carries out linear transformations on the entire data set to attain vectors (identified as eigenvalues), then identifies incremental ...