What a difficult question; is there a strict definition of the quantile
function we could grab from statistics or something?
Given you example I want to ask: what is more important; the number of
classifications, or the fact that they are "even" in size...
If we go for even in size; you may get 2 categories when you asked for three
Quantile( {0 0 0 0 3 5 7 9}, 2) ==> {0 0 0 0}, { 3 5 7 9 }
Quantile( {0 0 0 0 3 5 7 9}, 3) ==> {0 0 0 0}, { 3 5 7 9 }
This may be a strange case of what do you expect? If I am looking at a
map of summary of I want to know what the colors represent; and if I ask
the application to color equal quantities of data in different colors;
for the data you provided we could only make a map with 2 categories;
anything else would be a mistake ...
So while I can think of silly ways to break the content up into {0 0}
and {0 0} - they are just that - silly.
Jody
Andrea Aime wrote:
> Hi,
> I'm having some troubles using the quantile classification algorithm.
> As you may know, quantile figures out how to classify a range of numbers
> in a way that each class has the same number of features in it.
>
> Consider a case when an attribute has the following values (in different
> features): {0 0 0 0 3 5 7 9}. Then ask the quantile classifier to create
> a 4 intervals classification, and you'll get:
> {0 0}
> {0 0}
> {3 5}
> {7 9}
> This does not look very nice... I'm wondering if the quantile algorithm
> should consider this and avoid breaking the classes when the the same
> value will keep on appearing on the next class. For most users the
> following classification:
> att < 3
> 3 < att <= 5
> 5 < att
> or put another way:
> {0 0 0 0}
> {3 5}
> {7 9}
> thought not made of 3 intervals, would make much more sense.
> What I'm wondering is, can we have a quantile function that returns
> eventually less intervals but that does not builds odd classes like
> the current one?
>
> Cheers
> Andrea
>
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