How important is set.seed

My inclination is to follow Jeff?s advice and put it at the beginning of
the program.

You can always experiment:



set.seed(42)

rnorm(5,5,5)

rnorm(5,5,5)

runif(5,0,3)



As long as the commands are executed in the order they are written, then
the outcome is the same every time. Set seed is giving you reproducible
outcomes. However, the second rnorm() does not give you the same outcome as
the first. So set seed starts at the same point but if you want the first
and second rnorm() call to give the same results you will need another
set.seed(42).



Note also, that it does not matter if you pause: run the above code as a
chunk, or run each command individually you get the same result (as long as
you do it in the sequence written). So, if you set seed, run some code,
take a break, come back write some more code you  might get in trouble
because R is still using the original set.seed() command.

To solve this issue use

set.seed(Sys.time())



Or



set.seed(NULL)



Some of this is just good programming style workflow:



Import data

Declare variables and constants (set.seed() typically goes here)

Define functions

Body of code

Generate output

Clean up ( set.seed(NULL) would go here, along with removing unused
variables and such)



Regards,

Tim



*From:* Neha gupta <neha.bologna90 at gmail.com>
*Sent:* Tuesday, March 22, 2022 10:48 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]*


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

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