How to setup PCA (Principal Components Analysis) in the Data Science Toolkit
Modified on: Mon, 22 Jan, 2018 at 4:39 PM
Purpose: The purpose of PCA analysis is to convert a set of columns that are possibly correlated into a set of uncorrelated variables called principal components. It makes sense to combine multiple columns into principal components in order to speed up the run time of a model, reduce noise or otherwise optimize a model. PCA is one step in the predictive modeling process.