[R] How important is set.seed
tebert @end|ng |rom u||@edu
Tue Mar 22 01:44:17 CET 2022
If you are using the program for data analysis then set.seed() is not necessary unless you are developing a reproducible example. In a standard analysis it is mostly counter-productive because one should then ask if your presented results are an artifact of a specific seed that you selected to get a particular result. However, in cases where you need a reproducible example, debugging a program, or specific other cases where you might need the same result with every run of the program then set.seed() is an essential tool.
From: R-help <r-help-bounces using r-project.org> On Behalf Of Jeff Newmiller
Sent: Monday, March 21, 2022 8:41 PM
To: r-help using r-project.org; Neha gupta <neha.bologna90 using gmail.com>; r-help mailing list <r-help using r-project.org>
Subject: Re: [R] How important is set.seed
First off, "ML models" do not all use random numbers (for prediction I would guess very few of them do). Learn and pay attention to what the functions you are using do.
Second, if you use random numbers properly and understand the precision that your specific use case offers, then you don't need to use set.seed. However, in practice, using set.seed can allow you to temporarily avoid chasing precision gremlins, or set up specific test cases for testing code, not results. It is your responsibility to not let this become a crutch... a randomized simulation that is actually sensitive to the seed is unlikely to offer an accurate result.
Where to put set.seed depends a lot on how you are performing your simulations. In general each process should set it once uniquely at the beginning, and if you use parallel processing then use the features of your parallel processing framework to insure that this happens. Beware of setting all worker processes to use the same seed.
On March 21, 2022 5:03:30 PM PDT, Neha gupta <neha.bologna90 using gmail.com> wrote:
>I want to know
>(1) In which cases, we need to use set.seed while building ML models?
>(2) Which is the exact location we need to put the set.seed function i.e.
>when we split data into train/test sets, or just before we train a model?
> [[alternative HTML version deleted]]
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Sent from my phone. Please excuse my brevity.
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