On 13 March 2012 18:16, Andreas Mueller wrote:
> On 03/13/2012 07:49 AM, Olivier Grisel wrote:
> > Le 12 mars 2012 17:49, Robert Layton a écrit :
> >> I'll work off that template, and when I work out the details of the
> >> shrinking parameters (specifically which one is more in use), I'll
> bra
On 03/13/2012 07:49 AM, Olivier Grisel wrote:
> Le 12 mars 2012 17:49, Robert Layton a écrit :
>> I'll work off that template, and when I work out the details of the
>> shrinking parameters (specifically which one is more in use), I'll branch
>> and submit a PR.
> Great. I think the nearest centro
> Great. I think the nearest centroid is a very nice baseline classifier
> for sanity check: fast to fit, fast to predict, zero hyper-paramete
> and yet make reasonable assumption for many classification dataset (a
> good example of high bias, low variance, the opposite of deep decision
> trees or
Le 12 mars 2012 17:49, Robert Layton a écrit :
>
> I'll work off that template, and when I work out the details of the
> shrinking parameters (specifically which one is more in use), I'll branch
> and submit a PR.
Great. I think the nearest centroid is a very nice baseline classifier
for sanity c
On 13 March 2012 09:42, Olivier Grisel wrote:
> Le 11 mars 2012 20:35, Robert Layton a écrit :
> > Hi All,
> >
> > On reading some research, it appears that the shrunken centroid
> classifier
> > is one of the better methods for authorship analysis.
> > Therefore, I'm going to implement it at se
Le 11 mars 2012 20:35, Robert Layton a écrit :
> Hi All,
>
> On reading some research, it appears that the shrunken centroid classifier
> is one of the better methods for authorship analysis.
> Therefore, I'm going to implement it at see if it really is, and I was
> planning to add it to scikits.l
On 12 March 2012 19:30, Andreas wrote:
> **
> Hi Robert.
> To me, this sounds somwhat like Linear Discriminant Analysis or rather
> Quadratic Discriminant Analysis (without the shrinking part) to me.
>
> In these methods, a Gaussian is fitted to each class and classification
> is done by finding
Hi Robert.
To me, this sounds somwhat like Linear Discriminant Analysis or rather
Quadratic Discriminant Analysis (without the shrinking part) to me.
In these methods, a Gaussian is fitted to each class and classification
is done by finding the Gaussian that most likely created a data point.
Thi
