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

What is PCA in the Data Science Toolkit?

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 a one step in the predictive modeling process.  

Additional Information on PCA

• What is PCA
• Number of Principal Components
• What is an eigen value

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

Example:

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 Ruths.ai on Vimeo.

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