The purpose of this article is to explain how to set up PCA (Principal Component Analysis)
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.
- Convert categorical variable to binary columns
- Only runs on complete case data, so is very sensitive to missing data
- Only runs on numeric data, so categorical variables would need to be converted to binary columns
Steps to Run:
- Go to the Tools menu
- Select the Data Science option
- Select the PCA option
- Enter the data table, columns to be included and a tolerance if desired
- Data table
- Columns that would be put into a predictive model (remember the PCA will attempt to combine)
- Option to Add All column or Clear All columns checked.
- Tolerance - cutoff in variance (from 0 to 1) of the first PC, reduces the number of PCs returned. Leave blank to not enforce a tolerance cutoff.
- Click Ok when finishing selection.
- 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 table for PC
How to Filter a subset of a data
- Open the filter panel by clicking the filter icon on the top bar
- Choose the correct filtering scheme
- Click "Refresh data table" icon on PC Values on Original Data.
- Note: This also syncs with all the other visualization on PCA.
See PCA in action video below
Data Science Toolkit: PCA from Ruths.ai on Vimeo.
For additional information on RAI Data Science Toolkit documentation, click here.