Mailman 3 October 2020 - scikit-learn

Question RE: skLearn Logistic Regression
by The Helmbolds March 6, 2021

March 6, 2021

I have a case with binary results and 1-D features, like: X = np.array(-3,-2,-1,0,1,2,3,) and y = np.array(0, 0, 0, 1, 1, 1, 1) only longer arrays (about 180 entries in each array) of this general type. So this should be the "simplest" case. Altho I've tried several variations of the Logistic input formats, in LogisticRegression.fit(X, y) they keep being rejected with the most common error message being Missing argument y I assure you I do indeed have an array "y" that is passed to "fit" So----What do I have to do to get Logistic Regression to accept 1-D features?

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scikit-learn monthly meeting October 26th 2020
by Chiara Marmo Oct. 23, 2020

Oct. 23, 2020

Dear list, The next scikit-learn monthly meeting will take place on Monday October 26th at 11AM 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/47swZeY3STqn5rjtkS0mDg#Contributors>" section, of the public pad: https://hackmd.io/47swZeY3STqn5rjtkS0mDg Best Chiara

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Re: [scikit-learn] scikit-learn Digest, Vol 55, Issue 5
by Maiia Bakhova Oct. 22, 2020

Oct. 22, 2020

Hello Julio, Thanks for your feedback! The Elbow method may produce plots with concavity changing from one point to a next. It means that some points must be removed from consideration, and my method takes care of this. In addition the math is adapted for clustering procedure and much simpler. This helps with efficiency and maintenance. Best regards, Mya On Thu, Oct 22, 2020 at 9:04 AM <scikit-learn-request(a)python.org> wrote: > Send scikit-learn mailing list submissions to > scikit-learn(a)python.org > > To subscribe or unsubscribe via the World Wide Web, visit > https://mail.python.org/mailman/listinfo/scikit-learn > or, via email, send a message with subject or body 'help' to > scikit-learn-request(a)python.org > > You can reach the person managing the list at > scikit-learn-owner(a)python.org > > When replying, please edit your Subject line so it is more specific > than "Re: Contents of scikit-learn digest..." > > > Today's Topics: > > 1. Numeric version of Elbow method for finding an optimal > cluster number (Maiia Bakhova) > 2. Re: Numeric version of Elbow method for finding an optimal > cluster number (Julio Antonio Soto) > > > ---------------------------------------------------------------------- > > Message: 1 > Date: Wed, 21 Oct 2020 10:48:19 -0700 > From: Maiia Bakhova <myabakhova(a)gmail.com> > To: scikit-learn(a)python.org > Subject: [scikit-learn] Numeric version of Elbow method for finding an > optimal cluster number > Message-ID: > < > CAC7JaAoY0Ub8zKUa0A_w0aUJCV5vG4M4FBtTNH1N-EXP-4se0w(a)mail.gmail.com> > Content-Type: text/plain; charset="utf-8" > > Hello, > Sorry, this is not about scikit-learn. It is my method which I once offered > here, and was told that > it is too easy to code. Since then I have been talking to other people and > learn that linguists > do not understand enough math to work it out, and even people who are good > at math would prefer > a ready script. I wrote one and would like to share. > I did a numeric implementation of the Elbow method for calculating the > optimal cluster number. I will appecuat it greatly if you show my work here > and people would give me feedback on it. > https://github.com/Mathemilda/Numeric_ElbowMethod_For_K-means > Best, > Mya > -- > Maiia Bakhova > Mathematician in Data Science > http://myabakhova.blogspot.com > https://www.linkedin.com/in/myabakhova >

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Numeric version of Elbow method for finding an optimal cluster number
by Maiia Bakhova Oct. 21, 2020

Oct. 21, 2020

Hello, Sorry, this is not about scikit-learn. It is my method which I once offered here, and was told that it is too easy to code. Since then I have been talking to other people and learn that linguists do not understand enough math to work it out, and even people who are good at math would prefer a ready script. I wrote one and would like to share. I did a numeric implementation of the Elbow method for calculating the optimal cluster number. I will appecuat it greatly if you show my work here and people would give me feedback on it. https://github.com/Mathemilda/Numeric_ElbowMethod_For_K-means Best, Mya -- Maiia Bakhova Mathematician in Data Science http://myabakhova.blogspot.com https://www.linkedin.com/in/myabakhova

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About the Boston housing prices dataset
by Olivier Grisel Oct. 14, 2020

Oct. 14, 2020

Hi all, Thanks to the sustained effort of several contributors (thanks Maria and Lucy in particular), the Boston housing price dataset is no longer used in the examples of scikit-learn (nor in the test suite) in the master branch. To give some context on why this dataset is problematic, please have a look at this discussion and the blog post linked in it: https://github.com/scikit-learn/scikit-learn/issues/16155 Now that we no longer use sklearn.datasets.load_boston internally, we have to make a decision about what to do with the loader function itself: deprecate it? just silently hide it from our documentation from our documentation (probably a bad idea)? keep it but educate our users about its ethical problem? Personally, I would be slightly in favor of the latter option and I drafted a short paragraph here: https://github.com/scikit-learn/scikit-learn/pull/18594#issuecomment-707601… Please feel free to share your thoughts so that we can hopefully make a consensual decision before the 0.24 release. Regards, -- Olivier

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Rerunning Kmeans with Python
by Stephen Malcolm Oct. 5, 2020

Oct. 5, 2020

Hello all, I've written some code to run Kmeans on a data set (please see below). And I've plotted the results, with my two clusters/ centroids. However, I've to re-run Kmeans several times and pull up different plots (showing the different centroid positions). Can someone point me in the right direction how to write this extra code to perform this task? Then I've to conclude if Kmeans is stable. I believe this is the lowest sum of squared errors? Thanking you in advance. #pandas used to read dataset and return the data #numpy and matplotlib to represent and visualize the data #sklearn to implement kmeans algorithm import pandas as pd import numpy as np import matplotlib.pyplot as plt from sklearn.cluster import KMeans from sklearn.preprocessing import StandardScaler #import the data data = pd.read_csv('file.csv') #extract values x=data['V1'] y=data['V2'] V1_V2 = np.column_stack ((V1, V2)) km_res = KMeans (n_clusters= 2).fit(V1_V2) y_kmeans = km_res.predict(V1_V2) plt.scatter(V1, V2, c=y_kmeans, cmap='viridis', s = 50, alpha = 0.5) plt.xlabel('V1') plt.ylabel('V2') plt.title('Visualization of raw data'); clusters = km_res.cluster_centers_ plt.scatter(clusters[:,0], clusters[:,1], c='blue', s=150) Get Outlook for iOS<https://aka.ms/o0ukef>

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