The purpose of this article is to explain PCA (Principal Component Analysis)

What is PCA in the Data Science Toolkit?


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 a one step in the predictive modeling process.  

Additional Information on PCA

Outputs: (4 Visualization):

  • PC Values on Original Data - Scatter Plot
  • PCA Rotation Matrix table
  • Variance explained by each PC bar chart. There will be one bar for each principal component.
  • PCA Plot for top components - Scatter Plot

Note: Columns are also appended to the original data for PC


Data Science Toolkit PCA User Guide: How to set up PCA (Principal Components Analysis)

See the PCA in action below:

Data Science Toolkit: PCA from on Vimeo.

For additional information on RAI Data Science Toolkit documentation, click here.