The interesting point Nick Psaris makes in "Fun Q" is that he has a
combination mean/mode function which chooses one or the other depending on
the type of the argument: floating point args return the mean, integer args
return the mode.  This is particularly interesting because, in the context
of machine learning, this allows him to combine both classification and
regression ML models into his nearest-neighbor finder.

On Fri, Jul 24, 2020 at 3:08 PM John Randall <[email protected]>
wrote:

> A mode of a sample is not ill-defined, it is just not unique.  Neither is
> a median, if you are interested in probability.
>
> You can encapsulate mode, median and mean in a single formula.  Suppose y
> is a data vector and t is a scalar.  The a value of t which minimizes +/
> (|y-t)^i [with special meaning for x^0: see below] is
> - A mode of y if i=0.
> -A median of y if i=1.
> -The mean of y if i=2.
> Here x^0 meas 1 if x=0 and 0 if x is nonzero.
>
> John
> ________________________________
> From: Programming <[email protected]> on behalf of
> Devon McCormick <[email protected]>
> Sent: Friday, July 24, 2020 2:35 PM
> To: J-programming forum <[email protected]>
> Subject: [Jprogramming] Standard library version of statistical "mode"?
>
> Hi - I've started reading "Fun Q" which is a book on machine learning using
> the q language.  Early on, the author points out that his "mode" function -
> where "mode" is stats-talk for "the most frequent observation" - is
> order-dependent.
>
> I checked my own "mode" and found that this is true of mine as well:
> mode
>    ~. {~ [: (i. >./) #/.~
>    mode 1 2 2 3 3
> 2
>    mode 1 3 3 2 2
> 3
>
> This might be an ill-defined statistical concept but does anyone have any
> insight based on practice?  Is this order-dependence just a weakness of the
> definition of "mode"?
>
> I could not find "mode" defined in any of the J standard libraries.
>
> Thanks,
>
> Devon
>
> --
>
> Devon McCormick, CFA
>
> Quantitative Consultant
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-- 

Devon McCormick, CFA

Quantitative Consultant
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