Re: [Scikit-learn-general] Critical Difference Diagram

> hypothesis can not be rejected? > > Only looking at ranks also seems to discard a lot of information > > Here is the reference (I think): > http://www.jmlr.org/papers/v7/demsar06a.html > > Seems pretty well-cited. > > > > On 10/29/2015 09:28 AM, Dayvid Victo

[Scikit-learn-general] Critical Difference Diagram

R object [it is not as simple as it should be to connect using rpy2]. Thanks, -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc in Computer Science at Federal University of Pernambuco (UFPE) BSc in Computer Engineering - Federal

[Scikit-learn-general] datasets for research

There aren't many links (sadly, most of them aren't github users), but I hope it might help someone here! Also, feel free to add any cathegory/repository you are using! Thanks, -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc

Re: [Scikit-learn-general] feature selection

There are feature selection algorithms based on Evolutionary Algorithms, so, despite the exponential space of search, you can fix a number of evaluations. Experimentally, this approach have found optimal solutions on Instace/Feature/Classifier selection, without exploring the whole search space.

[Scikit-learn-general] Dynamic Multiple Classifier Systems

, -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc in Computer Science at Federal University of Pernambuco (UFPE) BSc in Computer Engineering - Federal University of Pernambuco (UFPE

[Scikit-learn-general] Random Subspace Ensemble Method

is welcome). Thanks, -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc in Computer Science at Federal University of Pernambuco (UFPE) BSc in Computer Engineering - Federal University of Pernambuco (UFPE

Re: [Scikit-learn-general] Evaluation measure for imbalanced data

@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc in Computer Science at Federal University of Pernambuco (UFPE) BSc in Computer Engineering

Re: [Scikit-learn-general] Evaluation measure for imbalanced data

than two classes as well. G-Mean = sqrt(TP_rate x TN_rate) On 23.07.2014, at 19:46, Dayvid Victor victor.d...@gmail.com wrote: Mario, as I said, the correct would be: - AUC = (1 + TP_rate - FP_rate) / 2 But you are also right, that is another evaluation metric stated in those references

Re: [Scikit-learn-general] Handle sparse data on Instance Reduction

could change to this approach! Again, thanks! On Fri, Jul 4, 2014 at 5:28 AM, Olivier Grisel olivier.gri...@ensta.org wrote: 2014-07-04 3:35 GMT+02:00 Dayvid Victor victor.d...@gmail.com: Hi Olivier, I solved this issue, but talking to some people in the maillist, they adviced me to start

Re: [Scikit-learn-general] Handle sparse data on Instance Reduction

Also, is there any plans to add a transform(X, y, axis=1) in the pipe? Is anybody working on that? Thanks, On Fri, Jul 4, 2014 at 10:29 AM, Dayvid Victor victor.d...@gmail.com wrote: Hi Olivier, Thank you for your considerations. I'll follow your recomendations. Instance Reduction

Re: [Scikit-learn-general] Handle sparse data on Instance Reduction

?id=111408631iu=/4140/ostg.clktrk ___ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general -- *Dayvid Victor R. de Oliveira* PhD Candidate in Computer Science

Re: [Scikit-learn-general] Implementation of new Anomaly/Outlier Detection Algorithms

Hi Nicholas, I have some works on outlier detection, let me know how can I help, once you start this project. Thanks, via Mobile. Dayvid V. R. Oliveira PhD candidate in Computer Science - UFPE MSc in Computer Science - UFPE Computer Engineer - UFPE On Jun 23, 2014 12:52 PM, Nicolas Goix

Re: [Scikit-learn-general] Instance Reduction on scikit-learn

orthogonal set of algorithms (which may not be seminal enough for inclusion anyway) seems unlikely at present. I suggest you make the instance reduction directory a separate repository, and invite comment there. Cheers, - Joel On 19 June 2014 11:46, Dayvid Victor victor.d...@gmail.com wrote

Re: [Scikit-learn-general] Instance Reduction on scikit-learn

to maintenance costs). Given that the technique seems to be KNN-focused, it could be incorporated as an extension to the KNN classifier/regressor classes, by providing an option to reduce the population when fitting, rather than a set of separate estimators. On 20 June 2014 09:30, Dayvid Victor victor.d

Re: [Scikit-learn-general] Instance Reduction on scikit-learn

. Thanks, On Wed, Jun 18, 2014 at 2:45 PM, Kyle Kastner kastnerk...@gmail.com wrote: Do you have any references for this technique? What is it typically used for? On Wed, Jun 18, 2014 at 12:26 PM, Dayvid Victor victor.d...@gmail.com wrote: Hi there, Is anybody working on an Instance

[Scikit-learn-general] Instance Reduction on scikit-learn

to contribute to this module, please, reach me in my email! https://github.com/dvro/scikit-learn/tree/instance_reduction/sklearn/instance_reduction Thanks, -- Dayvid Victor R. de Oliveira PhD Candidate in Computer Science at Federal University of Pernambuco (UFPE) MSc in Computer Science

Re: [Scikit-learn-general] SMOTE

Hi There, I am working on Instance Reduction (noise removal) on sklearn as my first contribution (hopefully) and I know a SMOTE implementation would be very helpful. Checkout Garcia et all Evolutionary Based IR for imbalanced Datasets. It concludes that SMOTE oversampling is very helpful for

[Scikit-learn-general] Handle sparse data on Instance Reduction

, but I do not think that's the case. Thanks, -- Dayvid Victor R. de Oliveira MSc Candidate in Computer Science at Federal University of Pernambuco (UFPE) BSc in Computer Engineering - Federal University of Pernambuco (UFPE