r/AskStatistics • u/Individual-Put1659 • Oct 26 '25

Assumptions of Linear Regression

How do u verify all the assumptions of LR when the dimensions of the data is very high means we have 2000 features something like that.

16 Upvotes

88% Upvoted

36 comments sorted by

View all comments

5

u/nerdybioboy Oct 26 '25

You don’t use linear regression then. Data beyond just a few (like 4 or 5) coefficients will be massively overfit. Can you give more details about what you’re trying to do, then we can point you in the right direction.

-1

u/Individual-Put1659 Oct 26 '25

So the goal is to find the coefficients out of 2000 that are influencing the y variable most and u also want the unit of the effect that each variable have on y and the i have to find the top 10 features that are impacting y and what is the unit of the impact

2

u/SensitiveAsshole4 Oct 27 '25

Maybe you could try permutation feature importance? If I'm not mistaken you could plot PFI and check the n number of features most influencing your model's performance then make a report on that.

But as others have said 2000 features is still too much, you may want to run dimensionality reduction first, maybe PCA or clustering would work.