🌐 Mailman 3 August 2020 - scikit-learn - python.org

New Contributor: Greetings from Brazil
by Ivomar Brito Soares Aug. 31, 2020

Aug. 31, 2020

Hello everyone, My name is Ivomar and I am a machine learning engineer | data scientist from Brazil. I have been working with machine learning since 2013 and I am a frequent user of scikit-learn. I decided to start contributing to scikit-learn to deepen my knowledge of machine learning. I am very happy to be part of this community and hope to be interacting with you all. Best regards, -- Ivomar Brito Soares https://www.linkedin.com/in/ivomar-brito-soares-26b3b9151/

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Generating biased dataset using make_classification
by Saha, Prashanta Aug. 31, 2020

Aug. 31, 2020

To generate a biased dataset using the make_classfication method which parameter is needed to be used? Thanks.

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Some typos in documentation and errors
by 최우정 Aug. 30, 2020

Aug. 30, 2020

To whom it may concern I am a good user of scikit-learn. First of all, I am grateful to scikit-learn for providing a good service. The reason I write an email is to report some typos. While studying about AUC, I think I found some typos in API documentation. As it is written in " https://scikit-learn.org/stable/modules/model_evaluation.html#roc-metrics" One-vs-one Algorithm, the multiclass macro AUC metric was defined in the reference [HT2001]. But there is a double difference in macro ovo AUC between the reference and documentation. Furthermore, under the macro AUC, there is weighted AUC which is defined in the reference [FC2009] as it is written in documentation. But there is no same metric in the documentation in the reference however there is a similar one, AU1P. After reviewing the code which includes the definition of roc_auc_score, I notice that it is different from both of the expressions in the documentation and the reference. Additionally, my scikit-learn version is 0.23.1. I hope this part will be fixed well. Thank you for reading my email.

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scikit-learn monthly meeting August 31st
by Chiara Marmo Aug. 29, 2020

Aug. 29, 2020

Dear list, The next scikit-learn monthly meeting will take place on Monday August 31st at 12PM UTC: https://www.timeanddate.com/worldclock/meetingdetails.html?year=2020&month=… While these meetings are mainly for core-devs to discuss the current topics, we are also happy to welcome non-core devs and other project maintainers. Feel free to join, using the following link: https://meet.google.com/xhq-yoga-rtf If you plan to attend and you would like to discuss something specific about your contribution please add your name (or github pseudo) in the " Contributors <https://hackmd.io/AuqfmgwvTf-bFz60yjVG1g?view#Contributors>" section, of the public pad: https://hackmd.io/AuqfmgwvTf-bFz60yjVG1g Best, Chiara

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Feature Request
by Jayanth B Aug. 25, 2020

Aug. 25, 2020

Is there a class / module for Hierarchical Divisive Clustering in sci-kit learn ? -- Jayanth Boddu | 7710092075

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Python Version Support Policy
by Martin Thoma Aug. 21, 2020

Aug. 21, 2020

Hi :-) Did you discuss at some point a policy which Python versions you want to support? I see that scikit-learn supports at the moment 3.6, 3.7, and 3.8 (badge in README). In a couple of weeks (October?) there will be 3.9, but I don't see any issue opened discussing 3.9 A minor python increment (e.g. 3.8 -> 3.9) will now happen every year, IIRC. So maybe it would be nice to just say "we support the latest 3 Python versions". In the very least, this would mean that wheels are published and that the CI pipeline is run for the newer versions. I think it would also be nice to "deprecate" old features and drop support explicitly. For example, if scikit-learn dropped support for 3.6 we could use future annotations. The implication of such a drop of support would be a major version increase. Best regards, Martin

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Imputers and DataFrame objects
by Ram Rachum Aug. 19, 2020

Aug. 19, 2020

Hey guys, This is a bit of a complicated question. I was helping my friend do a task with Pandas/sklearn for her data science class. I figured it'll be a breeze, since I'm fancy-pancy Python programmer. Oh wow, it was so not. I was trying to do things that felt simple to me, but there were so many problems, I spent 2 hours and only had a partial solution. I'm wondering whether I'm missing something. She got a CSV with lots of data about cars. Some of the data had missing values (marked with "?"). Additionally, some columns had small numbers written as strings like "one", "two", "three", etc. There were maybe a few more issues like these. The task was to remove these irregularities. So for the "?" items, replace them with mean, and for the "one", "two" etc. replace with a numerical value. I could easily write my own logic that does that, but she told me I should use the tools that come with sklearn: SimpleImputer, OneHotEncoder, BinaryEncoder for the "one" "two" "three". They gave me so, so many problems. For one, I couldn't figure out how to apply SimpleImputer on just one column in the DataFrame, and then get the results in the form of a dataframe. (Either changing in-place or creating a new DataFrame.) I think I spent an hour on this problem alone. Eventually I found a way <https://www.dropbox.com/preview/Desktop/Shani/floof.py>, but it definitely felt like I was doing something wrong, like this is supposed to be simpler. Also, when trying to use BinaryEncoder for "one" "two" "three", it raised an exception because there were NaN values there. Well, I wanted to first convert them to real numbers and then use the same SimpleImputer to fix these. But I couldn't, because of the exception. Any insight you could give me would be useful. Thanks, Ram.

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Fwd: [Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative
by Adrin Aug. 18, 2020

Aug. 18, 2020

FYI: Related to the data-frame like discussions we've been having. ---------- Forwarded message --------- From: Ralf Gommers <ralf.gommers(a)gmail.com> Date: Mon., Aug. 17, 2020, 22:35 Subject: [Numpy-discussion] start of an array (tensor) and dataframe API standardization initiative To: Discussion of Numerical Python <numpy-discussion(a)python.org> Hi all, I'd like to share this announcement blog post about the creation of a consortium for array and dataframe API standardization here: https://data-apis.org/blog/announcing_the_consortium/. It's still in the beginning stages, but starting to take shape. We have participation from one or more maintainers of most array and tensor libraries - NumPy, TensorFlow, PyTorch, MXNet, Dask, JAX, Xarray. Stephan Hoyer, Travis Oliphant and myself have been providing input from a NumPy perspective. The effort is very much related to some of the interoperability work we've been doing in NumPy (e.g. it could provide an answer to what's described in https://numpy.org/neps/nep-0037-array-module.html#requesting-restricted-sub… ). At this point we're looking for feedback from maintainers at a high level (see the blog post for details). Also important: the python-record-api tooling and data in its repo has very granular API usage data, of the kind we could really use when making decisions that impact backwards compatibility. Cheers, Ralf _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)python.org https://mail.python.org/mailman/listinfo/numpy-discussion

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ANN: Welcoming Christian Lorentzen and Juan Carlos Alfaro Jiménez
by Nicolas Hug Aug. 17, 2020

Aug. 17, 2020

The core developers of Scikit-learn have recently voted to welcome Christian Lorentzen to the core dev team, and Juan Carlos Alfaro Jiménez to the triage team, in recognition of their efforts and trustworthiness as contributors. Congratulations to you both and thank you for your contributions! Welcome!

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Opinion on reference mentioning that RF uses weak learners
by Fernando Marcos Wittmann Aug. 17, 2020

Aug. 17, 2020

Hello guys, The the following reference states that Random Forests uses weak learners: - https://blog.citizennet.com/blog/2012/11/10/random-forests-ensembles-and-pe… The random forest starts with a standard machine learning technique called > a “decision tree” which, in ensemble terms, corresponds to our weak learner. ... Thus, in ensemble terms, the trees are weak learners and the random forest > is a strong learner. I completely disagree with that statement. But I would like the opinion of the community to double check if I am not missing something.

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