Re: [Scikit-learn-general] [GSoC2015 Improve GMM module]

Hi Olivier, Loïc, Andreas and group, I have been thinking over the API convention for GMM. The discussion on issue #2473 , #4062 points out the inconsistency on ``score_ sample``, `

Re: [Scikit-learn-general] GSoC Community Bonding

Hi Gael ​ My GSoC blog url is http://barmaley-exe.blogspot.com As required, there's relevant tag gsoc15 On Mon, May 25, 2015 at 3:08 PM, Gael Varoquaux < gael.varoqu...@normalesup.org> wrote: > Hi GSOC students, > > And welcome. I hope that you will have a fun and productive summer. > > To commun

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Re: [Scikit-learn-general] Scitkit-learn Random Forest Classifier

Thank you for the answer!!! I thought that this parameter just looks into my features? For example: weight, width, height... and so on. Cause I'm using fingerprints as features, and every bit is a single feature (the fingerprints are about 2048 bits long). So i have for every fingerprints 2048 fe

Re: [Scikit-learn-general] Scitkit-learn Random Forest Classifier

Hi, You can control the number of attributes that is drawn (tested) at each node with the max_features parameters. Best regards, Arnaud Joly > On 27 May 2015, at 11:47, Herbert Schulz wrote: > > Hello everyone, > > I'm using the "Random Forest Classifier" to predict the toxicity of a > comp

[Scikit-learn-general] [GSoC2015 metric learning]

Dear Artem, congratulations on the acceptance of your GSoC proposal! I am certain there will be a very interesting summer ahead of us. Kyle and I are excited to be mentors and will do our best to provide all the guidance necessary for your project to succeed. It is very rich and will be a great ad

[Scikit-learn-general] [GSoC2015 metric learning]

Dear Artem, congratulations on the acceptance of your GSoC proposal! I am certain there will be a very interesting summer ahead of us. Kyle and I are excited to be mentors and will do our best to provide all the guidance necessary for your project to succeed. It is very rich and will be a great ad

[Scikit-learn-general] Scitkit-learn Random Forest Classifier

Hello everyone, I'm using the "Random Forest Classifier" to predict the toxicity of a compound. Is there a way to use an attribute selection with different set of attributes for each tree node? I have this option in Knime, now I'm trying to implement it in python. I'll be very grateful for you