From research1msn at gmail.com Fri Jul 16 10:33:37 2021 From: research1msn at gmail.com (research njagi) Date: Fri, 16 Jul 2021 17:33:37 +0300 Subject: [scikit-learn] Help with a bug Message-ID: kindly help me fix this bug ImportError: cannot import name 'DecesionTreeClassifier' from 'sklearn.tree' (C:\Users\ASUS\Anaconda3\lib\site-packages\sklearn\tree\__init__.py) -------------- next part -------------- An HTML attachment was scrubbed... URL: From mitalikatoch at gmail.com Fri Jul 16 10:32:49 2021 From: mitalikatoch at gmail.com (mitali katoch) Date: Fri, 16 Jul 2021 16:32:49 +0200 Subject: [scikit-learn] Help with a bug In-Reply-To: References: Message-ID: Hey, Use correct name: DecisionTreeClassifier All the best! On Fri, Jul 16, 2021, 16:30 research njagi wrote: > kindly help me fix this bug > > ImportError: cannot import name 'DecesionTreeClassifier' from 'sklearn.tree' (C:\Users\ASUS\Anaconda3\lib\site-packages\sklearn\tree\__init__.py) > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From nadim.farhat at gmail.com Fri Jul 16 10:33:16 2021 From: nadim.farhat at gmail.com (Farhat, Nadim) Date: Fri, 16 Jul 2021 10:33:16 -0400 Subject: [scikit-learn] Help with a bug In-Reply-To: References: Message-ID: <691683AC-AF67-492B-BFE3-6260238BB9E0@hxcore.ol> An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Sun Jul 25 07:22:35 2021 From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Sun, 25 Jul 2021 13:22:35 +0200 Subject: [scikit-learn] scikit-learn monthly meeting: Monday 26 July 2021 - 8 pm UTC Message-ID: Dear all, The scikit-learn developer monthly meeting will take place on Monday July 26th at 8 pm UTC - Video call link: https://meet.google.com/qbg-ucpe-ngz - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q - Local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month =6&day=28&hour=15&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 The goal of this meeting is to discuss ongoing development topics for the project. Everybody is welcome. As usual, please follow the code of conduct of the project: https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md Regards, -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Sun Jul 25 08:27:42 2021 From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Sun, 25 Jul 2021 14:27:42 +0200 Subject: [scikit-learn] scikit-learn monthly meeting: Monday 26 July 2021 - 8 pm UTC In-Reply-To: References: Message-ID: Please find the correct local times: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=7&day=26&hour=20&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 On Sun, 25 Jul 2021 at 13:22, Guillaume Lema?tre wrote: > Dear all, > > The scikit-learn developer monthly meeting will take place on Monday > July 26th at 8 pm UTC > > - Video call link: https://meet.google.com/qbg-ucpe-ngz > - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q > - Local times: > https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month > =6&day=28&hour=15&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 > > The goal of this meeting is to discuss ongoing development topics for > the project. Everybody is welcome. > > As usual, please follow the code of conduct of the project: > https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md > > Regards, > -- > Guillaume Lemaitre > Scikit-learn @ Inria Foundation > https://glemaitre.github.io/ > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From gael.varoquaux at normalesup.org Mon Jul 26 17:26:19 2021 From: gael.varoquaux at normalesup.org (Gael Varoquaux) Date: Mon, 26 Jul 2021 23:26:19 +0200 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length Message-ID: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> This email is meant for the scikit-learn Technical Committee, and is on the public mailing list for transparency. The community has not been able to reach a strong consensus on an incredibly important decision: line length :) https://doodle.com/poll/wpp7c8343zy46v93?utm_source=poll&utm_medium=link So, the TCs need to make a vote. Members of the TC, please vote below by adding your name: Keep current 88 characters: Revert to 79 characters: As a reminder, the technical committee is made of: Alexandre Gramfort, Olivier Grisel, Adrin Jalali, Andreas M?ller, Joel Nothman, Hanmin Qin, Ga?l Varoquaux, and Roman Yurchak (according to https://scikit-learn.org/stable/governance.html) We have one week to vote, but if we do it in less time, no one will complain. Thanks! Ga?l -- Gael Varoquaux Research Director, INRIA Visiting professor, McGill http://gael-varoquaux.info http://twitter.com/GaelVaroquaux From alexandre.gramfort at inria.fr Tue Jul 27 03:54:05 2021 From: alexandre.gramfort at inria.fr (Alexandre Gramfort) Date: Tue, 27 Jul 2021 09:54:05 +0200 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length In-Reply-To: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> References: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> Message-ID: > Keep current 88 characters: > Revert to 79 characters: Alex Gramfort From adrin.jalali at gmail.com Tue Jul 27 05:00:14 2021 From: adrin.jalali at gmail.com (Adrin) Date: Tue, 27 Jul 2021 11:00:14 +0200 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length In-Reply-To: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> References: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> Message-ID: > Keep current 88 characters: > Revert to 79 characters: Adrin Jalali On Tue, Jul 27, 2021 at 1:03 AM Gael Varoquaux < gael.varoquaux at normalesup.org> wrote: > This email is meant for the scikit-learn Technical Committee, and is on > the public mailing list for transparency. > > The community has not been able to reach a strong consensus on an > incredibly important decision: line length :) > https://doodle.com/poll/wpp7c8343zy46v93?utm_source=poll&utm_medium=link > > So, the TCs need to make a vote. Members of the TC, please vote below by > adding your name: > > Keep current 88 characters: > > Revert to 79 characters: > > As a reminder, the technical committee is made of: Alexandre Gramfort, > Olivier Grisel, Adrin Jalali, Andreas M?ller, Joel Nothman, Hanmin Qin, > Ga?l Varoquaux, and Roman Yurchak (according to > https://scikit-learn.org/stable/governance.html) > > We have one week to vote, but if we do it in less time, no one will > complain. > > Thanks! > > Ga?l > > -- > Gael Varoquaux > Research Director, INRIA Visiting professor, McGill > http://gael-varoquaux.info http://twitter.com/GaelVaroquaux > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From solegalli at protonmail.com Tue Jul 27 05:08:18 2021 From: solegalli at protonmail.com (Sole Galli) Date: Tue, 27 Jul 2021 09:08:18 +0000 Subject: [scikit-learn] random forests and multil-class probability Message-ID: Hello community, Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? Thank you Sole -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Tue Jul 27 05:22:10 2021 From: g.lemaitre58 at gmail.com (=?utf-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Tue, 27 Jul 2021 11:22:10 +0200 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: References: Message-ID: > On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn wrote: > > Hello community, > > Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. Each decision tree of the forest is natively supporting multi class. > > The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? According to the above answer, the sum for each row of the array given by `predict_proba` will sum to 1. According to the documentation, the probabilities are computed as: The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. > > Thank you > Sole > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn From solegalli at protonmail.com Tue Jul 27 05:31:36 2021 From: solegalli at protonmail.com (Sole Galli) Date: Tue, 27 Jul 2021 09:31:36 +0000 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: References:

Message-ID: Thank you! I was confused because in the multiclass documentation it says that for those estimators that have multiclass support built in, like Decision trees and Random Forests, then we do not need to use the wrapper classes like the OnevsRest. Thus I have the following question, if I want to determine the PR curves or the ROC curve, say with micro-average, do I need to wrap them with the 1 vs rest? Or it does not matter? The probability values do change slightly. Thank you! ??????? Original Message ??????? On Tuesday, July 27th, 2021 at 11:22 AM, Guillaume Lema?tre wrote: > > On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn scikit-learn at python.org wrote: > > > > Hello community, > > > > Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. > > Each decision tree of the forest is natively supporting multi class. > > > The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? > > According to the above answer, the sum for each row of the array given by `predict_proba` will sum to 1. > > According to the documentation, the probabilities are computed as: > > The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. > > > Thank you > > > > Sole > > > > scikit-learn mailing list > > > > scikit-learn at python.org > > > > https://mail.python.org/mailman/listinfo/scikit-learn From niourf at gmail.com Tue Jul 27 05:33:02 2021 From: niourf at gmail.com (Nicolas Hug) Date: Tue, 27 Jul 2021 10:33:02 +0100 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: References:

Message-ID: <031152d2-ca59-69ee-b04c-125fda724105@gmail.com> To add to Guillaume's answer: the native multiclass support for forests/trees is described here: https://scikit-learn.org/stable/modules/tree.html#multi-output-problems It's not a one-vs-rest strategy and can be summed up as: > * > > Store n output values in leaves, instead of 1; > > * > > Use splitting criteria that compute the average reduction > across all n outputs. > Nicolas On 27/07/2021 10:22, Guillaume Lema?tre wrote: >> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn wrote: >> >> Hello community, >> >> Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. > Each decision tree of the forest is natively supporting multi class. > >> The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? > According to the above answer, the sum for each row of the array given by `predict_proba` will sum to 1. > According to the documentation, the probabilities are computed as: > > The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. > >> Thank you >> Sole >> >> >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn at python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... URL: From solegalli at protonmail.com Tue Jul 27 05:42:47 2021 From: solegalli at protonmail.com (Sole Galli) Date: Tue, 27 Jul 2021 09:42:47 +0000 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: <031152d2-ca59-69ee-b04c-125fda724105@gmail.com> References:

<031152d2-ca59-69ee-b04c-125fda724105@gmail.com> Message-ID: Thank you! So when in the multiclass document says that for the algorithms that support intrinsically multiclass, which are listed [here](https://scikit-learn.org/stable/modules/multiclass.html), when it says that they do not need to be wrapped by the OnevsRest, it means that there is no need, because they can indeed handle multi class, each one in their own way. But, if I want to plot PR curves or ROC curves, then I do need to wrap them because those metrics are calculated as a 1 vs rest manner, and this is not how it is handled by the algos. Is my understanding correct? Thank you! ??????? Original Message ??????? On Tuesday, July 27th, 2021 at 11:33 AM, Nicolas Hug wrote: > To add to Guillaume's answer: the native multiclass support for forests/trees is described here: https://scikit-learn.org/stable/modules/tree.html#multi-output-problems > > It's not a one-vs-rest strategy and can be summed up as: > >>> - >>> >>> Store n output values in leaves, instead of 1; >>> >>> - >>> >>> Use splitting criteria that compute the average reduction across all n outputs. > > Nicolas > > On 27/07/2021 10:22, Guillaume Lema?tre wrote: > >>> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn >>> [](mailto:scikit-learn at python.org) >>> wrote: >>> >>> Hello community, >>> >>> Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. >> >> Each decision tree of the forest is natively supporting multi class. >> >>> The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? >> >> According to the above answer, the sum for each row of the array given by `predict_proba` will sum to 1. >> According to the documentation, the probabilities are computed as: >> >> The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. >> >>> Thank you >>> Sole >>> >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn at python.org >>> >>> https://mail.python.org/mailman/listinfo/scikit-learn >> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn at python.org >> >> https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Tue Jul 27 06:02:23 2021 From: g.lemaitre58 at gmail.com (=?utf-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Tue, 27 Jul 2021 12:02:23 +0200 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: References:

<031152d2-ca59-69ee-b04c-125fda724105@gmail.com> Message-ID: <7D53A0FD-EB5E-4C27-966B-D6954EEF7398@gmail.com> As far that I remember, `precision_recall_curve` and `roc_curve` do not support multi class. They are design to work only with binary classification. Then, we provide an example for precision-recall that shows one way to compute precision-recall curve via averaging: https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ > On 27 Jul 2021, at 11:42, Sole Galli via scikit-learn wrote: > > Thank you! > > So when in the multiclass document says that for the algorithms that support intrinsically multiclass, which are listed here , when it says that they do not need to be wrapped by the OnevsRest, it means that there is no need, because they can indeed handle multi class, each one in their own way. > > But, if I want to plot PR curves or ROC curves, then I do need to wrap them because those metrics are calculated as a 1 vs rest manner, and this is not how it is handled by the algos. Is my understanding correct? > > Thank you! > > ??????? Original Message ??????? > On Tuesday, July 27th, 2021 at 11:33 AM, Nicolas Hug wrote: >> To add to Guillaume's answer: the native multiclass support for forests/trees is described here: https://scikit-learn.org/stable/modules/tree.html#multi-output-problems >> It's not a one-vs-rest strategy and can be summed up as: >> >> >>> Store n output values in leaves, instead of 1; >>> >>> Use splitting criteria that compute the average reduction across all n outputs. >>> >> >> >> Nicolas >> >> On 27/07/2021 10:22, Guillaume Lema?tre wrote: >>>> On 27 Jul 2021, at 11:08, Sole Galli via scikit-learn

wrote: >>>> >>>> Hello community, >>>> >>>> Do I understand correctly that Random Forests are trained as a 1 vs rest when the target has more than 2 classes? Say the target takes values 0, 1 and 2, then the model would train 3 estimators 1 per class under the hood?. >>> Each decision tree of the forest is natively supporting multi class. >>> >>>> The predict_proba output is an array with 3 columns, containing the probability of each class. If it is 1 vs rest. am I correct to assume that the sum of the probabilities for the 3 classes should not necessarily add up to 1? are they normalized? how is it done so that they do add up to 1? >>> According to the above answer, the sum for each row of the array given by `predict_proba` will sum to 1. >>> According to the documentation, the probabilities are computed as: >>> >>> The predicted class probabilities of an input sample are computed as the mean predicted class probabilities of the trees in the forest. The class probability of a single tree is the fraction of samples of the same class in a leaf. >>> >>>> Thank you >>>> Sole >>>> >>>> >>>> >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn at python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn at python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn -------------- next part -------------- An HTML attachment was scrubbed... URL: From jbbrown at kuhp.kyoto-u.ac.jp Tue Jul 27 06:42:28 2021 From: jbbrown at kuhp.kyoto-u.ac.jp (Brown J.B.) Date: Tue, 27 Jul 2021 12:42:28 +0200 Subject: [scikit-learn] random forests and multil-class probability In-Reply-To: <7D53A0FD-EB5E-4C27-966B-D6954EEF7398@gmail.com> References:

<031152d2-ca59-69ee-b04c-125fda724105@gmail.com> <7D53A0FD-EB5E-4C27-966B-D6954EEF7398@gmail.com> Message-ID: 2021?7?27?(?) 12:03 Guillaume Lema?tre : > As far that I remember, `precision_recall_curve` and `roc_curve` do not > support multi class. They are design to work only with binary > classification. > Correct, the TPR-FPR curve (ROC) was originally intended for tuning a free parameter, in signal detection, and is a binary-type metric. For ML problems, it lets you tune/determine an estimator's output value threshold (e.g., a probability or a raw discriminant value such as in SVM) for arriving an optimized model that will be used to give a final, binary-discretized answer in new prediction tasks. Hope this helps, J.B. -------------- next part -------------- An HTML attachment was scrubbed... URL: From joel.nothman at gmail.com Tue Jul 27 09:10:17 2021 From: joel.nothman at gmail.com (Joel Nothman) Date: Tue, 27 Jul 2021 23:10:17 +1000 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length In-Reply-To: References: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> Message-ID: > > Keep current 88 characters > Joel Nothman (though admittedly not strong!) > > Revert to 79 characters: > Alex Gramfort Adrin Jalali -------------- next part -------------- An HTML attachment was scrubbed... URL: From g.lemaitre58 at gmail.com Wed Jul 28 03:35:43 2021 From: g.lemaitre58 at gmail.com (=?UTF-8?Q?Guillaume_Lema=C3=AEtre?=) Date: Wed, 28 Jul 2021 09:35:43 +0200 Subject: [scikit-learn] scikit-learn monthly meeting: Monday 26 July 2021 - 8 pm UTC In-Reply-To: References: Message-ID: Dear all, Please find the notes of our monthly meeting: https://github.com/scikit-learn/administrative/blob/master/meeting_notes/2021-07-26.md Cheers, On Sun, 25 Jul 2021 at 14:27, Guillaume Lema?tre wrote: > Please find the correct local times: > > https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021&month=7&day=26&hour=20&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 > > On Sun, 25 Jul 2021 at 13:22, Guillaume Lema?tre > wrote: > >> Dear all, >> >> The scikit-learn developer monthly meeting will take place on Monday >> July 26th at 8 pm UTC >> >> - Video call link: https://meet.google.com/qbg-ucpe-ngz >> - Meeting notes / agenda: https://hackmd.io/0yokz72CTZSny8y3Re648Q >> - Local times: >> https://www.timeanddate.com/worldclock/meetingdetails.html?year=2021& >> month >> =6&day=28&hour=15&min=0&sec=0&p1=1440&p2=240&p3=248&p4=195&p5=179&p6=224 >> >> The goal of this meeting is to discuss ongoing development topics for >> the project. Everybody is welcome. >> >> As usual, please follow the code of conduct of the project: >> https://github.com/scikit-learn/scikit-learn/blob/main/CODE_OF_CONDUCT.md >> >> Regards, >> -- >> Guillaume Lemaitre >> Scikit-learn @ Inria Foundation >> https://glemaitre.github.io/ >> > > > -- > Guillaume Lemaitre > Scikit-learn @ Inria Foundation > https://glemaitre.github.io/ > -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/ -------------- next part -------------- An HTML attachment was scrubbed... URL: From reshama.stat at gmail.com Wed Jul 28 07:32:33 2021 From: reshama.stat at gmail.com (Reshama Shaikh) Date: Wed, 28 Jul 2021 07:32:33 -0400 Subject: [scikit-learn] [Data Umbrella] scikit-learn Latin America sprint (June 2021) In-Reply-To: References: Message-ID: Hello, The report from the Data Umbrella Latin America scikit-learn sprint is here [a]. 40 people, including 7 countries in Latin America, joined. TAKEAWAYS - translations are useful. English is not the lingua franca in all regions where python/data science is practiced. - videos are accessible references, but they also get outdated quickly. - both translations and videos are useful, and also resource-intensive. Thanks to everyone on the Data Umbrella and scikit-learn teams for their assistance in making this happen! [a]: https://reshamas.github.io/data-umbrella-latam-2021-scikit-learn-sprint-report/ Cheers, Reshama --- Reshama Shaikh she/her On Sun, May 23, 2021 at 10:37 AM Reshama Shaikh wrote: > Hello, > Data Umbrella is organizing a scikit-learn open source sprint, with a > focus on * LATIN AMERICA * on June 26, 2021. > > This scikit-learn sprint is a 4-hour online hands-on "hackathon" where we > work on issues in the scikit-learn GitHub repo to get started in > contributing to open source in a structured setting. There is 2-3 hours of > pre-work and participants will work with a pair programming partner during > the sprint. > > Sprint website: > https://latam2021.dataumbrella.org/home > > Sprint application: > https://forms.gle/cxav9eE9ZkiULfbm7 > > Tweet: > https://twitter.com/DataUmbrella/status/1394661734275821573 > > LinkedIn post: > https://www.linkedin.com/feed/update/urn:li:activity:6800434144624070656/ > > Meetup event: > https://www.meetup.com/data-umbrella/events/278298428/ > > FAQ: > Q: If I am not in Latin America, can I still apply and participate? > > A: Priority will be given to folks in the Latin America region. If there > are open spots, we will open it up to other regions. In the meantime, we > have resources available for all to get started in contributing to > scikit-learn. Check them out here: > https://latam2021.dataumbrella.org/about/prep-work > > Any questions, please email us: data.umbrella.dei at gmail.com > > Cheers, > Reshama > --- > Reshama Shaikh > she/her > Blog | Twitter > | LinkedIn | GitHub > > > Data Umbrella > NYC PyLadies > > > -------------- next part -------------- An HTML attachment was scrubbed... URL: From adrin.jalali at gmail.com Wed Jul 28 09:19:43 2021 From: adrin.jalali at gmail.com (Adrin) Date: Wed, 28 Jul 2021 15:19:43 +0200 Subject: [scikit-learn] [Data Umbrella] scikit-learn Latin America sprint (June 2021) In-Reply-To: References: Message-ID: Thanks for doing this Reshama. On Wed, Jul 28, 2021 at 1:33 PM Reshama Shaikh wrote: > Hello, > The report from the Data Umbrella Latin America scikit-learn sprint is > here [a]. > > 40 people, including 7 countries in Latin America, joined. > > TAKEAWAYS > - translations are useful. English is not the lingua franca in all > regions where python/data science is practiced. > - videos are accessible references, but they also get outdated quickly. > - both translations and videos are useful, and also resource-intensive. > > Thanks to everyone on the Data Umbrella and scikit-learn teams for their > assistance in making this happen! > > [a]: > https://reshamas.github.io/data-umbrella-latam-2021-scikit-learn-sprint-report/ > > Cheers, > Reshama > --- > Reshama Shaikh > she/her > > > > On Sun, May 23, 2021 at 10:37 AM Reshama Shaikh > wrote: > >> Hello, >> Data Umbrella is organizing a scikit-learn open source sprint, with a >> focus on * LATIN AMERICA * on June 26, 2021. >> >> This scikit-learn sprint is a 4-hour online hands-on "hackathon" where we >> work on issues in the scikit-learn GitHub repo to get started in >> contributing to open source in a structured setting. There is 2-3 hours of >> pre-work and participants will work with a pair programming partner during >> the sprint. >> >> Sprint website: >> https://latam2021.dataumbrella.org/home >> >> Sprint application: >> https://forms.gle/cxav9eE9ZkiULfbm7 >> >> Tweet: >> https://twitter.com/DataUmbrella/status/1394661734275821573 >> >> LinkedIn post: >> https://www.linkedin.com/feed/update/urn:li:activity:6800434144624070656/ >> >> Meetup event: >> https://www.meetup.com/data-umbrella/events/278298428/ >> >> FAQ: >> Q: If I am not in Latin America, can I still apply and participate? >> >> A: Priority will be given to folks in the Latin America region. If there >> are open spots, we will open it up to other regions. In the meantime, we >> have resources available for all to get started in contributing to >> scikit-learn. Check them out here: >> https://latam2021.dataumbrella.org/about/prep-work >> >> Any questions, please email us: data.umbrella.dei at gmail.com >> >> Cheers, >> Reshama >> --- >> Reshama Shaikh >> she/her >> Blog | Twitter >> | LinkedIn >> | GitHub >> >> >> Data Umbrella >> NYC PyLadies >> >> >> _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: From olivier.grisel at ensta.org Wed Jul 28 12:34:47 2021 From: olivier.grisel at ensta.org (Olivier Grisel) Date: Wed, 28 Jul 2021 18:34:47 +0200 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length In-Reply-To: References: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org> Message-ID: Many very active core devs not represented in the TC voted for 88 and my previous vote for 79 was not that strong. So I feel that I should now vote for 88: Keep current 88 characters: Olivier Revert to 79 characters: -- Olivier From christophe at pallier.org Wed Jul 28 12:41:06 2021 From: christophe at pallier.org (Christophe Pallier) Date: Wed, 28 Jul 2021 18:41:06 +0200 Subject: [scikit-learn] [TC Vote] Technical Committee vote: line length In-Reply-To: References: <20210726212619.54iy56wbl4sdbe3z@phare.normalesup.org>

Message-ID: https://en.m.wikipedia.org/wiki/Punched_card On Wed, 28 Jul 2021, 18:35 Olivier Grisel, wrote: > Many very active core devs not represented in the TC voted for 88 and > my previous vote for 79 was not that strong. So I feel that I should > now vote for 88: > > Keep current 88 characters: > > Olivier > > Revert to 79 characters: > -- > Olivier > _______________________________________________ > scikit-learn mailing list > scikit-learn at python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -------------- next part -------------- An HTML attachment was scrubbed... URL: