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
See the PCA in action below:
For additional information on RAI Data Science Toolkit documentation, click here.