Mailman 3 February 2023 - scikit-learn

[ANN] scikit-learn 0.24.0rc1 is online!
by Olivier Grisel Jan. 19, 2024

Jan. 19, 2024

Please help us test the first release candidate for scikit-learn 0.24.0: pip install scikit-learn==0.24.0rc1 Changelog: https://scikit-learn.org/0.24/whats_new/v0.24.html In particular, if you maintain a project with a dependency on scikit-learn, please let us know about any regression. Feel free to also retweet the announcement to get more people to test it before the final release (probably in 1 week or 2): https://twitter.com/scikit_learn/status/1334562221498753026 Thanks to anybody who helped make this happen! -- Olivier

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[ANNOUNCEMENT] scikit-learn 1.0 release
by Adrin June 30, 2023

June 30, 2023

Hi everyone, We're happy to announce the 1.0 release which you can install via pip or conda: pip install -U scikit-learn or conda install -c conda-forge scikit-learn You can read the release highlights under https://scikit-learn.org/stable/auto_examples/release_highlights/plot_relea… and the long list of the changes under https://scikit-learn.org/stable/whats_new/v1.0.html New major features include: mandatory keyword arguments in many places, Spline Transformers, Quantile Regressor, Feature Names Support, a more flexible plotting API, Online One-Class SVM, and much more! This version supports Python versions 3.7 to 3.9. A big thanks to all contributors for making this release possible. Regards, Adrin, On the behalf of the scikit-learn maintainer team.

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classification model that can handle missing values w/o learning from missing values
by Martin Gütlein March 10, 2023

March 10, 2023

Hi, I am looking for a classification model in python that can handle missing values, without imputation and "without learning from missing values", i.e. without using the fact that the information is missing for the inference. Explained with the help of decision trees: * The algorithm should NOT learn whether missing values should go to the left or right child (like the HistGradientBoostingClassifier). * Instead it could built the prediction for each child node and aggregate these (like some Random Forest implementations do). If that is not possible in sci-kit learn, maybe you have already discussed this? Or you know of a fork of sci-kit learn that is able to do this, or some other python library? Any help would be really appreciated, kind regards, Martin P.S. Here is my use-case, in case you are interested: I have a binary classification problem with a positive and a negative class, and two types of features A and B. In my training data, I have a lot more data (90%) where B is missing. In my test data, I always have B, which is good because the B features are better than the A features. In the cases where B is present in the training data, the ratio of positive examples is much higher than when its missing. So what HistGradientBoostingClassifier does, it uses the fact that B is not missing in the test data, and predicts way too many positives. (Additionally, some feature values of type A are also often missing)

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[ANN] scikit-learn 1.1.3 is online!
by Guillaume Lemaître March 9, 2023

March 9, 2023

scikit-learn 1.1.3 is out on pypi.org and conda-forge! This bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2 and wheels for Python 3.11. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead. More details in the changelog: https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-3 You can upgrade with pip as usual: pip install -U scikit-learn The conda-forge builds will be available shortly, which you can then install using: conda install -c conda-forge scikit-learn Thanks again to all the contributors. On behalf of the scikit-learn maintainer team. -- Guillaume Lemaitre Scikit-learn @ Inria Foundation https://glemaitre.github.io/

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mutual information for continuous variables with scikit-learn
by m m Feb. 1, 2023

Feb. 1, 2023

Hello, I have two continuous variables (heart rate samples over a period of time), and would like to compute mutual information between them as a measure of similarity. I've read some posts suggesting to use the mutual_info_score from scikit-learn but will this work for continuous variables? One stackoverflow answer suggested converting the data into probabilities with np.histogram2d() and passing the contingency table to the mutual_info_score. from sklearn.metrics import mutual_info_score def calc_MI(x, y, bins): c_xy = np.histogram2d(x, y, bins)[0] mi = mutual_info_score(None, None, contingency=c_xy) return mi # generate data L = np.linalg.cholesky( [[1.0, 0.60], [0.60, 1.0]]) uncorrelated = np.random.standard_normal((2, 300)) correlated = np.dot(L, uncorrelated) A = correlated[0] B = correlated[1] x = (A - np.mean(A)) / np.std(A) y = (B - np.mean(B)) / np.std(B) # calculate MI mi = calc_MI(x, y, 50) Is calc_MI a valid approach? I'm asking because I also read that when variables are continuous, then the sums in the formula for discrete data become integrals, but I'm not sure if this procedure is implemented in scikit-learn? Thanks!

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