# Picking more than desired number of features in PCA

I have encountered the presentation and one of the ideas mentioned there is as follows. Suppose, that there is a sample of objects with 100 features, only 5 of which are informative. On the 5th slide it recommends to extract ~20 of these features by PCA, as the informative features are likely to keep the desired 5.

Can anyone explain the nature of this recommendation? Why don't we extract exactly 5 features by PCA? Or at least 10, as intuitively 20 seems to be a bit overkill.

• Keep in mind that none of the PCA features map directly to your original features in a 1:1 manner. Each PCA feature is a combination of your original features. You'll almost certainly need more than 5 PCA features to capture all of the variance in your original important features. – Nuclear Wang Apr 20 '16 at 14:38

## 1 Answer

I think that this one is just a rough estimate as the following lines states that keeping 20 axes is a lot better than having the 100 initial variables and not that far from 5 (if you kept only the 'good' initial variables). 20 is high enough to ensure capturing the 5 but low enough so that PCA is still a pertinent approach.

What I usually do to decide on the number of dimensions to keep is just according to the percentage of inertia I want to keep (95% often times).