.
On Wed, Mar 25, 2015 at 9:16 AM, Luca Puggini wrote:
> Hi guys,
> thanks for the interest.
>
> Some comments below
>
> Message: 1
>
>> Date: Tue, 24 Mar 2015 16:32:40 -0400
>> From: Andy
>> Subject: Re: [Scikit-learn-general] My personal suggestion
Hi guys,
thanks for the interest.
Some comments below
Message: 1
> Date: Tue, 24 Mar 2015 16:32:40 -0400
> From: Andy
> Subject: Re: [Scikit-learn-general] My personal suggestion regarding
> topics for GSoC (and my official application :-) )
> To: sciki
Hi Luca.
If you give write comment permissions, I could comment on the google doc
in-place which might be helpful.
As I think was commented earlier, the current PLS already implements
NIPALS. What would the addition be?
Use that in PCA? That is not super clear from the proposal.
I think impleme
Hi Luca.
Have you had a look at the top of
https://github.com/scikit-learn/scikit-learn/wiki/Google-summer-of-code-%28GSOC%29-2015
?
For an application, it is expected that you submit some patches to the
repo to get familiar with the codebase.
What is your github handle (I might have overlook
Thanks a lot for the material provided on randomized pca and random forest
it would for sure help me in my research.
I talked with my supervisor and he said that I am free to apply for this
summer project.
I used quiet a lot GAM and I did some work related to high dimensional
fault detection syst
Yes, in fact I did something similar in my thesis. See section 7.2 for
a discussion about this. Figure 7.5 is similar to what you describe in
your sample code. By varying the depth, you can basically control the
bias.
http://orbi.ulg.ac.be/bitstream/2268/170309/1/thesis.pdf
On 6 March 2015 at 13:5
After a little simulated study I agree with the previous comment.
With the Extra trees classifier it is possible to reduce the bias.
Despite that the result is still biased.
Here the sample code:
http://jpst.it/x9Mv
Here a possible reference:
http://www.biomedcentral.com/1471-2105/8/25
Please t
Hi,
thanks a lot I was not aware of the randomized PCA.
Regarding random forest is there any paper or resource that you can suggest
me?
I tried to use the forest with max_features=1 but it was still biased.
I did not try with a limited depth.
Thanks a lot,
Luca
--
Hi Luca,
On 6 March 2015 at 11:09, Luca Puggini wrote:
> Hi,
> It seems to me that you are discussing topics that can be introduced in
> sklearn with GSoC.
>
> I use sklearn quiet a lot and there are a couple of things that I really
> miss in this library:
>
> 1- Nipals PCA.
> The current version
On Fri, Mar 6, 2015 at 11:09 AM, Luca Puggini wrote:
> Hi,
> It seems to me that you are discussing topics that can be introduced in
> sklearn with GSoC.
>
> I use sklearn quiet a lot and there are a couple of things that I really
> miss in this library:
>
> 1- Nipals PCA.
> The current version o
Hi,
It seems to me that you are discussing topics that can be introduced in
sklearn with GSoC.
I use sklearn quiet a lot and there are a couple of things that I really
miss in this library:
1- Nipals PCA.
The current version of PCA is too low for high dimensional dataset.
Suppose to have p=1
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