[R] How important is set.seed

Jin Li j|n||68 @end|ng |rom gm@||@com
Tue Mar 22 05:07:56 CET 2022

The answer may depend on the model type you are going to develop. For
predictive models, yes you do need it. The dependence of predictive
accuracy measures on random seeds and dependence of stabilized predictive
accuracy measures on random seeds have been demonstrated and discussed in
Spatial Predictive Modeling with R (doi:10.1201/9781003091776), where many
reproducible examples are provided for various predictive methods including
RF, GBM and SVM.
Hope this helps.

On Tue, Mar 22, 2022 at 11:51 AM Ebert,Timothy Aaron <tebert using ufl.edu> wrote:

> 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.
> Tim
> -----Original Message-----
> 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
> [External Email]
> 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:
> >Hello everyone
> >
> >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?
> >
> >Thank you
> >
> >       [[alternative HTML version deleted]]
> >
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Jin Li, PhD
Founder, Data2action, Australia

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