I mean exactly the opposite: where you can get improvements in precision at
no expense of recall (usually in the "uninteresting" corners of the plot).
 Am I making sense? I might be misunderstanding this.


On Wed, Jul 31, 2013 at 9:24 PM, Kyle Kastner <notifications@github.com>wrote:

> In examples/plot_precision_recall.py:
>
> > +
> > +Recall (:math:`R`) is a metric which is defined as the number of
> > +true positives (:math:`T_p`) over the number of true positives plus the number
> > +of false negatives (:math:`F_n`).
> > +
> > +:math:`R = \\frac{T_p}{T_p + F_n}`
> > +
> > +These quantities are also related to the (:math:`F_1`) score, which is
> > +defined as the 2 times precision times recall over precision plus recall.
> > +
> > +:math:`F1 = 2\\frac{P \\times R}{P+R}`
> > +
> > +It is important to note that the precision may not decrease with recall.
> > +The definition of precision (:math:`\\frac{T_p}{T_p + F_p}`) shows that
> > +lowering the threshold of a classifier may increase the denominator, by
> > +increasing the number of results returned.
>
> I am a little confused by what you mean by focusing on "horizontal or
> vertical segments" - those point to areas where precision is either
> affected greatly (vertical) or not affected (horizontal) by changes in
> recall, which would indicate something about the underlying ratio of true
> vs. false results? I.E. a sharp change means that the change in recall
> introduced "wrong results" (false positives), while a flat change means the
> increased recall introduced "true results" (true positives)?
>
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