When to use Principle Component Analysis
- Are you looking to decrease the amount of variables but having trouble determining which ones to eliminate entirely?
- Would you like to make sure that your variables have no interdependence?
- Would you consider making the independent variables less understandable in order to achieve this goal?
- When the DATA is MULI-VARIATE and NUMERIC
- When Number of FEATURES is LARGE
- When Data is Unimodal
- When CLASS labels are NOT present / ignored
- To VISUALIZE the data — top 2 or top 3 PC’s.
- To REDUCE #Dimensions/Features for next stages
- To REMOVE Noise in features and Outliers in data
If you think respond to all this question will be then ONLY you shall consider applying PCA over your dataset.
Example is also provided for the better understanding of the theory.
PCA explained in depth “A One-Stop Shop for Principal Component Analysis | by Matt Brems | Towards Data Science”
