Introduction

Principal Component Analysis (PCA) is a statistical process that converts a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables known as principal components.

To create your model, you must first complete the Model Properties tab. The Basic Options and Advanced Options tabs provide sufficient default settings to complete a job, but you can change those settings to meet your needs. You then run your job and a limited version of the resulting model appears on the Model Output tab; the complete output is available in the Machine Learning Model Management tool. If you are satisfied with the output of your model, you can then expose it and use it in a scoring dataflow.