[Scikit-learn-general] Label propagation on regression tacks

Hi, I'm trying to .score() semi_supervised.LabelPropagation on continuous labels but it's not working since BaseLabelPropagation is inheriting from ClassifierMixin. Despite that, it's written at label_propagation.html that it "Can be

Re: [Scikit-learn-general] Fast Johnson-Lindenstrauss Transform

> Date: Wed, 29 Oct 2014 14:57:45 +0100 > From: Olivier Grisel > Subject: Re: [Scikit-learn-general] Fast Johnson-Lindenstrauss > Transform > To: scikit-learn-general > Message-ID: > > Content-Type: text/plain; charset=UTF-8 > > Indeed this is quite a new method and we have a policy

Re: [Scikit-learn-general] Dealing with Categorical Variable in Random Forest

Hi, Michael: Thank you for your comment. Actually, I use one-hot coding strategy but I don't think it satisfactory. I do hope that Scikit-learn developer can improve it because it is a big issue for decision tree method. On Wed, Oct 29, 2014 at 12:18 PM, Michael Eickenberg < michael.eickenb...@gm

Re: [Scikit-learn-general] Dealing with Categorical Variable in Random Forest

Hi Xin, as far as I know the only ways of working around this problem right now are one-hot encoding or using integer numbers to represent your classes. The former augments your feature space but can cause biases if different categorical features can take different numbers of values (leading to mo

[Scikit-learn-general] Dealing with Categorical Variable in Random Forest

Hi,: I'm a fan of Scikit-learn and it is my favorite ML package. However, I found this package DOES NOT deal with categorical variable for tree-based method. So I need to convert categorical variable into dummy variable before I can use tree method. Actually, this is counterintuitive to the origi

Re: [Scikit-learn-general] Fast Johnson-Lindenstrauss Transform

Can you comment a bit how they combine the random sign matrix and the subsample random subsample fourrier basis? Best regards, Arnaud Joly On 29 Oct 2014, at 14:24, Michal Romaniuk wrote: > Hi everyone, > > I'm thinking of adding the Unrestricted Fast Johnson-Lindenstrauss > Transform [1] to

Re: [Scikit-learn-general] Fast Johnson-Lindenstrauss Transform

Indeed this is quite a new method and we have a policy to wait a bit to see if it's actually practically useful before including an implementation in the code base. Michal, if you have replicated the results of the paper in Python it would be interesting to publish your code in a scikit-learn styl

Re: [Scikit-learn-general] Fast Johnson-Lindenstrauss Transform

It would be nice to have it implemented in a sklearn.random_projections-compatible form, but is there reason to believe it is stable/popular enough for inclusion in the repo? On 30 October 2014 00:24, Michal Romaniuk wrote: > Hi everyone, > > I'm thinking of adding the Unrestricted Fast Johnson-

[Scikit-learn-general] Fast Johnson-Lindenstrauss Transform

Hi everyone, I'm thinking of adding the Unrestricted Fast Johnson-Lindenstrauss Transform [1] to the random_projections module and would like to ask if maybe someone is already working on this. (If you know of a competing algorithm that would be worth looking at, please let me know ;)) Thanks, M