suggested classification algorithm
Greetings, I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM: http://scikit-learn.org/stable/_images/sphx_glr _plot_classifier_comparison_001.png However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first. thanks in advance Thomas -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: tevang@pharm.uoa.gr tevang3@gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/
http://contrib.scikit-learn.org/imbalanced-learn/ might be of interest to you. On 14 November 2016 at 22:14, Thomas Evangelidis <tevang3@gmail.com> wrote:
Greetings,
I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
http://scikit-learn.org/stable/_images/sphx_glr_plot_ classifier_comparison_001.png
However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
--
======================================================================
Thomas Evangelidis
Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr
tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique ( http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations. On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com> wrote:
Greetings,
I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
http://scikit-learn.org/stable/_images/sphx_glr_plot_ classifier_comparison_001.png
However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
--
======================================================================
Thomas Evangelidis
Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr
tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
-- Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302
Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list. __________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org] On Behalf Of Fernando Marcos Wittmann Sent: Wednesday, November 16, 2016 3:11 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm ⚠ EXT MSG: Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique (http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations. On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com<mailto:tevang3@gmail.com>> wrote: Greetings, I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM: http://scikit-learn.org/stable/_images/<http://learn.org/stable/_images/>sphx_glr_plot_classifier_comparison_001.png However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first. thanks in advance Thomas -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: tevang@pharm.uoa.gr<mailto:tevang@pharm.uoa.gr> tevang3@gmail.com<mailto:tevang3@gmail.com> website: https://sites.google.com/site/thomasevangelidishomepage/ _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302 * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.
Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning-imbalanced-classes/
On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith@macys.com> wrote:
Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
__________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com
From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org] On Behalf Of Fernando Marcos Wittmann Sent: Wednesday, November 16, 2016 3:11 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm
⚠ EXT MSG: Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique (http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations.
On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com> wrote: Greetings,
I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
http://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_0...
However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
--
Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302
* This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments. _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
Guys thank you all for your hints! Practical experience is irreplaceable that's why I posted this query here. I could read all week the mailing list archives and the respective internet resources but still not find the key info I could potentially get by someone here. I did PCA on my training set (this one has 24 positive and 1278 negative observation) and projected the 19 features on the first 2 PCs, which explain 87.6 % of the variance in the data. Does this plot help to decide which classification algorithms and/or over- or under-sampling would be more suitable? https://dl.dropboxusercontent.com/u/48168252/PCA_of_features.png thanks for your advices Thomas On 16 November 2016 at 22:20, Sebastian Raschka <se.raschka@gmail.com> wrote:
Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning- imbalanced-classes/
On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith@macys.com> wrote:
Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
____________________________________________________________
Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com
From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith= macys.com@python.org] On Behalf Of Fernando Marcos Wittmann Sent: Wednesday, November 16, 2016 3:11 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm
⚠ EXT MSG: Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique ( http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations.
On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com> wrote: Greetings,
I want to design a program that can deal with classification problems of
__________________ the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
classifier_comparison_001.png
However, I assume that Nearest Neighbors would not be effective in my
case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
--
Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302
* This is an EXTERNAL EMAIL. Stop and think before clicking a link or
opening attachments.
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: tevang@pharm.uoa.gr tevang3@gmail.com website: https://sites.google.com/site/thomasevangelidishomepage/
The problem with your analysis is it doesn’t include anything but features. You may want to look at Nina Zumel and John Mount’s work on y-aware PCR and PCA, as well as y-aware feature scaling. http://www.win-vector.com/blog/2016/05/pcr_part1_xonly/ http://www.win-vector.com/blog/2016/05/pcr_part2_yaware/ http://www.win-vector.com/blog/2016/06/y-aware-scaling-in-context/ __________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org] On Behalf Of Thomas Evangelidis Sent: Thursday, November 17, 2016 9:01 AM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm ⚠ EXT MSG: Guys thank you all for your hints! Practical experience is irreplaceable that's why I posted this query here. I could read all week the mailing list archives and the respective internet resources but still not find the key info I could potentially get by someone here. I did PCA on my training set (this one has 24 positive and 1278 negative observation) and projected the 19 features on the first 2 PCs, which explain 87.6 % of the variance in the data. Does this plot help to decide which classification algorithms and/or over- or under-sampling would be more suitable? https://dl.dropboxusercontent.com/u/48168252/PCA_of_features.png thanks for your advices Thomas On 16 November 2016 at 22:20, Sebastian Raschka <se.raschka@gmail.com<mailto:se.raschka@gmail.com>> wrote: Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning-imbalanced-classes/
On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith@macys.com<mailto:Dale.T.Smith@macys.com>> wrote:
Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
__________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com<mailto:dale.t.smith@macys.com>
From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith<mailto:scikit-learn-bounces%2Bdale.t.smith>=macys.com@python.org<mailto:macys.com@python.org>] On Behalf Of Fernando Marcos Wittmann Sent: Wednesday, November 16, 2016 3:11 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm
⚠ EXT MSG: Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique (http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations.
On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com<mailto:tevang3@gmail.com>> wrote: Greetings,
I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
http://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_0...
However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr<mailto:tevang@pharm.uoa.gr> tevang3@gmail.com<mailto:tevang3@gmail.com>
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
--
Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302<tel:%2B55%20%2819%29%20987-211302>
* This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments. _______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org<mailto:scikit-learn@python.org> https://mail.python.org/mailman/listinfo/scikit-learn -- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic email: tevang@pharm.uoa.gr<mailto:tevang@pharm.uoa.gr> tevang3@gmail.com<mailto:tevang3@gmail.com> website: https://sites.google.com/site/thomasevangelidishomepage/ * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.
One problem with the PCA approach is also that it doesn’t tell you how “discriminative” these features are in a >2 dimensional space, e.g., by a nonlinear model. Or in other words, I think it is hard to tell whether the class imbalance is a big problem in this task just from looking at a linear transformation and compression of the dataset. I think looking at confusion matrices and ROC curves for some models could help to determine if the class imbalance is a challenge for a learning algorithm in higher dimensional space?
On Nov 17, 2016, at 9:00 AM, Thomas Evangelidis <tevang3@gmail.com> wrote:
Guys thank you all for your hints! Practical experience is irreplaceable that's why I posted this query here. I could read all week the mailing list archives and the respective internet resources but still not find the key info I could potentially get by someone here.
I did PCA on my training set (this one has 24 positive and 1278 negative observation) and projected the 19 features on the first 2 PCs, which explain 87.6 % of the variance in the data. Does this plot help to decide which classification algorithms and/or over- or under-sampling would be more suitable?
https://dl.dropboxusercontent.com/u/48168252/PCA_of_features.png
thanks for your advices Thomas
On 16 November 2016 at 22:20, Sebastian Raschka <se.raschka@gmail.com> wrote: Yeah, there are many useful resources and implementations scattered around the web. However, a good, brief overview of the general ideas and concepts would be this one, for example: http://www.svds.com/learning-imbalanced-classes/
On Nov 16, 2016, at 3:54 PM, Dale T Smith <Dale.T.Smith@macys.com> wrote:
Unbalanced class classification has been a topic here in past years, and there are posts if you search the archives. There are also plenty of resources available to help you, from actual code on Stackoverflow, to papers that address various ideas. I don’t think it’s necessary to repeat any of this on the mailing list.
__________________________________________________________________________________________________________________________________________ Dale T. Smith | Macy's Systems and Technology | IFS eCom CSE Data Science 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.smith@macys.com
From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys.com@python.org] On Behalf Of Fernando Marcos Wittmann Sent: Wednesday, November 16, 2016 3:11 PM To: Scikit-learn user and developer mailing list Subject: Re: [scikit-learn] suggested classification algorithm
⚠ EXT MSG: Three based algorithms (like Random Forest) usually work well for imbalanced datasets. You can also take a look at the SMOTE technique (http://jair.org/media/953/live-953-2037-jair.pdf) which you can use for over-sampling the positive observations.
On Mon, Nov 14, 2016 at 9:14 AM, Thomas Evangelidis <tevang3@gmail.com> wrote: Greetings,
I want to design a program that can deal with classification problems of the same type, where the number of positive observations is small but the number of negative much larger. Speaking with numbers, the number of positive observations could range usually between 2 to 20 and the number of negative could be at least x30 times larger. The number of features could be between 2 and 20 too, but that could be reduced using feature selection and elimination algorithms. I 've read in the documentation that some algorithms like the SVM are still effective when the number of dimensions is greater than the number of samples, but I am not sure if they are suitable for my case. Moreover, according to this Figure, the Nearest Neighbors is the best and second is the RBF SVM:
http://scikit-learn.org/stable/_images/sphx_glr_plot_classifier_comparison_0...
However, I assume that Nearest Neighbors would not be effective in my case where the number of positive observations is very low. For these reasons I would like to know your expert opinion about which classification algorithm should I try first.
thanks in advance Thomas
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
--
Fernando Marcos Wittmann MS Student - Energy Systems Dept. School of Electrical and Computer Engineering, FEEC University of Campinas, UNICAMP, Brazil +55 (19) 987-211302
* This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments. _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
-- ====================================================================== Thomas Evangelidis Research Specialist CEITEC - Central European Institute of Technology Masaryk University Kamenice 5/A35/1S081, 62500 Brno, Czech Republic
email: tevang@pharm.uoa.gr tevang3@gmail.com
website: https://sites.google.com/site/thomasevangelidishomepage/
<PCA_of_features.png>_______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
participants (5)
-
Dale T Smith -
Fernando Marcos Wittmann -
Joel Nothman -
Sebastian Raschka -
Thomas Evangelidis