OK, I've opened a jira. https://issues.apache.org/jira/browse/SPARK-17718
And ok, I forgot the loss is summed in the objective function provided. My
mistake.
On a tangentially related topic, why is there a half in front of the
squared loss? Similarly, the L2 regularizer has a half. It's just a
constant and so the objective's minimum is not affected, but still curious
to know why the half wasn't left out.
On Mon, Sep 26, 2016 at 4:40 AM, Sean Owen <so...@cloudera.com> wrote:
> Yes I think that footnote could be a lot more prominent, or pulled up
> right under the table.
>
> I also think it would be fine to present the {0,1} formulation. It's
> actually more recognizable, I think, for log-loss in that form. It's
> probably less recognizable for hinge loss, but, consistency is more
> important. There's just an extra (2y-1) term, at worst.
>
> The loss here is per instance, and implicitly summed over all
> instances. I think that is probably not confusing for the reader; if
> they're reading this at all to double-check just what formulation is
> being used, I think they'd know that. But, it's worth a note.
>
> The loss is summed in the case of log-loss, not multiplied (if that's
> what you're saying).
>
> Those are decent improvements, feel free to open a pull request / JIRA.
>
>
> On Mon, Sep 26, 2016 at 6:22 AM, Tobi Bosede <ani.to...@gmail.com> wrote:
> > The loss function here for logistic regression is confusing. It seems to
> > imply that spark uses only -1 and 1 class labels. However it uses 0,1 as
> the
> > very inconspicuous note quoted below (under Classification) says. We
> need to
> > make this point more visible to avoid confusion.
> >
> > Better yet, we should replace the loss function listed with that for 0,
> 1 no
> > matter how mathematically inconvenient, since that is what is actually
> > implemented in Spark.
> >
> > More problematic, the loss function (even in this "convenient" form) is
> > actually incorrect. This is because it is missing either a summation
> (sigma)
> > in the log or product (pi) outside the log, as the loss for logistic is
> the
> > log likelihood. So there are multiple problems with the documentation.
> > Please advise on steps to fix for all version documentation or if there
> are
> > already some in place.
> >
> > "Note that, in the mathematical formulation in this guide, a binary label
> > y is denoted as either +1 (positive) or −1 (negative), which is
> convenient
> > for the formulation. However, the negative label is represented by 0 in
> > spark.mllib instead of −1, to be consistent with multiclass labeling."
>
