Hello Tim
In some of the examples I see in the tutorials, they put the random seed
just before the model training e.g train function in case of caret library.
Should I follow this?
Best regards
On Tuesday, March 22, 2022, Ebert,Timothy Aaron <tebert at ufl.edu> wrote:
> Ah, so maybe what you need is to think of ?set.seed()? as a treatment in
> an experiment. You could use a random number generator to select an
> appropriate number of seeds, then use those seeds repeatedly in the
> different models to see how seed selection influences outcomes. I am not
> quite sure how many seeds would constitute a good sample. For me that would
> depend on what I find and how long a run takes.
>
> In parallel processing you set seed in master and then use a random
> number generator to set seeds in each worker.
>
> Tim
>
>
>
> *From:* Neha gupta <neha.bologna90 at gmail.com>
> *Sent:* Tuesday, March 22, 2022 6:33 AM
> *To:* Ebert,Timothy Aaron <tebert at ufl.edu>
> *Cc:* Jeff Newmiller <jdnewmil at dcn.davis.ca.us>; r-help at
r-project.org
> *Subject:* Re: How important is set.seed
>
>
>
> *[External Email]*
>
> Thank you all.
>
>
>
> Actually I need set.seed because I have to evaluate the consistency of
> features selection generated by different models, so I think for this,
it's
> recommended to use the seed.
>
>
>
> Warm regards
>
> On Tuesday, March 22, 2022, Ebert,Timothy Aaron <tebert at 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 at r-project.org> On Behalf Of Jeff
Newmiller
> Sent: Monday, March 21, 2022 8:41 PM
> To: r-help at r-project.org; Neha gupta <neha.bologna90 at
gmail.com>; r-help
> mailing list <r-help at 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 at
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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