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
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 lucapug...@gmail.com 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
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
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
I thought you just wanted to mask some features, but I guess that was
not you intend.
You could make your code robust to future changes by using the
feature_indices_ attribute,
while assuming that the result first has all categorical, and then all
numerical values.
Btw, you might have an easier
Thanks for trying to make some time :)
On 03/06/2015 03:42 AM, Arnaud Joly wrote:
Hi,
Sadly this year, I won’t have time for mentoring.
However, I will try to find some spare time for reviewing!
Best regards,
Arnaud
On 05 Mar 2015, at 22:43, Andreas Mueller t3k...@gmail.com
On Fri, Mar 6, 2015 at 11:09 AM, Luca Puggini lucapug...@gmail.com 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
Well after a bit of tinkering it seems that OneHotEncoder has simple rules
to affect columns to the output:
1) first do the categorical, in the order given by the argument, creating
columns as needed by the values
2) then the numerical
So a piece of code like that seems to work:
fn = []
fc =
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
2015-03-05 16:57 GMT+01:00 Andy t3k...@gmail.com:
Well, the columns after the OneHotEncoder correspond to feature values,
not feature names, right?
Well, for the categorical ones this is right, except that not all my
features are categorical (hence the categorical_features=...) and they are
Hi,
Sadly this year, I won’t have time for mentoring.
However, I will try to find some spare time for reviewing!
Best regards,
Arnaud
On 05 Mar 2015, at 22:43, Andreas Mueller t3k...@gmail.com wrote:
Hi Wei Xue.
Thanks for your interest.
For the GMM project being familiar with DPGMM and
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