Can we use Principal Components(PCA) with other features?

Question

I have a dataset of 10 features. Three of these are categorical; when I apply one-hot encoding to these three, they blow up into 96 features. I reduced these 96 features into 20 by PCA.

I plan to use the 20 principal components and the remaining 7 features as my final feature set. Is this a good idea: to combine principal components with actual features?

Answer 1

PCA tends to represent a combination of actual features, most of the times this combination leads to some information loss. That usually is fair trade-off by the dimensionality reduction. Adding those actual features won't get you dimensionality too large and will get "back" some information lost by PCA.

But my advice would still be to try it both. and choose the one that leads better results (given your specification)