Scikit-learn: Difference between revisions

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| collapsible =

| author = [[David Cournapeau]]

| developer = [[Google Summer of Code]] project

| released = {{Start date and age|2007|06|df=yes}}

| latest release version = {{wikidata|property|reference|P348}}

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| website = {{URL|https://scikit-learn.org/}}

}}

{{Portal|Free and open-source software}}

'''scikit-learn''' (formerly '''scikits.learn''' and also known as '''sklearn''') is a [[free and open-source]] [[machine learning]] [[Library (computing)|library]] for the [[Python (programming language)|Python]] [[programming language]].<ref name="jmlr">{{cite journal

|author1=Fabian Pedregosa

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|volume=12

|pages=2825–2830

|arxiv=1201.0490

|bibcode=2011JMLR...12.2825P

|url=http://jmlr.org/papers/v12/pedregosa11a.html

}}</ref>

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==Overview==

The scikit-learn project started as scikits.learn, a [[Google Summer of Code]] project by French [[data scientist]] [[David Cournapeau]]. The name of the project stemsderives from theits notionrole that it isas a "SciKit"scientific (SciPytoolkit Toolkit),for amachine separatelylearning", originally developed and distributed as a third-party extension to [[SciPy]].<ref>{{cite web

|url=https://scikits.appspot.com/scikit-learn

|title=scikit-learn

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scikit-learn integrates well with many other Python libraries, such as [[Matplotlib]] and [[plotly]] for plotting, [[NumPy]] for array vectorization, [[Pandas (software)|Pandas]] dataframes, [[SciPy]], and many more.

== Version historyHistory ==

scikit-learn was initially developed by David Cournapeau as a Google Summer of Code project in 2007. Later that year, Matthieu Brucher joined the project and started to use it as a part of his thesis work. In 2010, [[French Institute for Research in Computer Science and Automation|INRIA]], the [[French Institute for Research in Computer Science and Automation]], got involved and the first public release (v0.1 beta) was published in late January 2010.

* August 2013. scikit-learn 0.14<ref name=":0" />

== Applications ==

* July 2014. scikit-learn 0.15.0<ref name=":0" />

Scikit-learn is widely used across industries for a variety of machine learning tasks such as classification, regression, clustering, and model selection. The following are real-world applications of the library:

* March 2015. scikit-learn 0.16.0<ref name=":0" />

* November 2015. scikit-learn 0.17.0<ref name=":0">{{Cite web|url=https://scikit-learn.org/dev/whats_new.html|title=Release history — scikit-learn 0.19.dev0 documentation|website=scikit-learn.org|access-date=2017-02-27}}</ref>

=== Finance and Insurance ===

* September 2016. scikit-learn 0.18.0

* July 2017. scikit-learn 0.19.0

* September'''AXA''' 2018.uses scikit-learn 0.20to speed up the compensation process for car accidents and to detect insurance fraud.0<ref name="sklearn-testimonials">{{citeCite web |title=Release History - 0.20.0 documentationTestimonials |url=https://scikit-learn.org/stable/whats_newtestimonials/testimonials.html#version-0-20 |website=scikit-learn.org |access-date=6 November 20182025-08-06}}</ref>

* '''Zopa''', a peer-to-peer lending platform, employs scikit-learn for credit risk modelling, fraud detection, marketing segmentation, and loan pricing.<ref name="sklearn-testimonials"/>

* May 2019. scikit-learn 0.21.0<ref>{{cite web |title=Release History - 0.21.0 documentation |url=https://scikit-learn.org/stable/whats_new.html#version-0-21-0 |website=scikit-learn |access-date=5 May 2019}}</ref>

* '''BNP Paribas Cardif''' uses scikit-learn to improve the dispatching of incoming mail and manage internal model risk governance through pipelines that reduce operational and overfitting risks.<ref name="sklearn-testimonials"/>

* December 2019. scikit-learn 0.22<ref>{{cite web |title=Release History - 0.22 documentation |url=https://scikit-learn.org/dev/whats_new/v0.22.html |website=scikit-learn |access-date=7 June 2020}}</ref>

* May 2020. scikit-learn 0.23.0<ref>{{cite web |title=Release History - 0.23.0 documentation |url=https://scikit-learn.org/dev/whats_new/v0.23.html#version-0-23-0 |website=scikit-learn |access-date=7 June 2020}}</ref>

* '''J.P. Morgan''' reports broad usage of scikit-learn across the bank for classification tasks and predictive analytics in financial decision-making.<ref name="sklearn-testimonials"/>

* Jan 2021. scikit-learn 0.24<ref>{{Citation|title=Release History - 0.24 documentation |url=https://scikit-learn.org/dev/whats_new/v0.24.html |website=scikit-learn |access-date=2021-02-08}}</ref>

* September 2021. scikit-learn 1.0.0<ref>{{cite web |title=Release History - 1.0.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-0 |website=scikit-learn }}</ref>

=== Retail and E-Commerce ===

* October 2021. scikit-learn 1.0.1<ref>{{cite web |title=Release History - 1.0.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html#version-1-0-1 |website=scikit-learn }}</ref>

* December 2021. scikit-learn 1.0.2<ref>{{cite web |title=Release History - 1.0.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.0.html |website=scikit-learn }}</ref>

* '''Booking.com''' uses scikit-learn for hotel and destination recommendation systems, fraudulent reservation detection, and workforce scheduling for customer support agents.<ref name="sklearn-testimonials"/>

* May 2022. scikit-learn 1.1.0<ref>{{cite web |title=Release History - 1.1.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-0 |website=scikit-learn }}</ref>

* '''HowAboutWe''' uses it to predict user engagement and preferences on a dating platform.<ref name="sklearn-testimonials"/>

* May 2022. scikit-learn 1.1.1<ref>{{cite web |title=Release History - 1.1.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-1 |website=scikit-learn }}</ref>

* '''Lovely''' leverages the library to understand user behaviour and detect fraudulent activity on its platform.<ref name="sklearn-testimonials"/>

* August 2022. scikit-learn 1.1.2<ref>{{cite web |title=Release History - 1.1.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html#version-1-1-2 |website=scikit-learn }}</ref>

* '''Data Publica''' uses it for customer segmentation based on the success of past partnerships.<ref name="sklearn-testimonials"/>

* October 2022. scikit-learn 1.1.3<ref>{{cite web |title=Release History - 1.1.3 documentation |url=https://scikit-learn.org/dev/whats_new/v1.1.html |website=scikit-learn }}</ref>

* December 2022. scikit-learn 1.2.0<ref>{{cite web |title=Release History - 1.2.0 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html#version-1-2-0 |website=scikit-learn }}</ref>

* '''Otto Group''' integrates scikit-learn throughout its data science stack, particularly in logistics optimization and product recommendations.<ref name="sklearn-testimonials"/>

* January 2023. scikit-learn 1.2.1<ref>{{cite web |title=Release History - 1.2.1 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html#version-1-2-1 |website=scikit-learn }}</ref>

* March 2023. scikit-learn 1.2.2<ref>{{cite web |title=Release History - 1.2.2 documentation |url=https://scikit-learn.org/dev/whats_new/v1.2.html |website=scikit-learn }}</ref>

=== Media, Marketing, and Social Platforms ===

* '''Spotify''' applies scikit-learn in its recommendation systems.<ref name="sklearn-testimonials"/>

* '''Betaworks''' uses the library for both recommendation systems (e.g., for Digg) and dynamic subspace clustering applied to weather forecasting data.<ref name="sklearn-testimonials"/>

* '''PeerIndex''' used scikit-learn for missing data imputation, tweet classification, and community clustering in social media analytics.<ref name="sklearn-testimonials"/>

* '''Bestofmedia Group''' employs it for spam detection and ad click prediction.<ref name="sklearn-testimonials"/>

* '''Machinalis''' utilizes scikit-learn for click-through rate prediction and relational information extraction for content classification and advertising optimization.<ref name="sklearn-testimonials"/>

* '''Change.org''' applies scikit-learn for targeted email outreach based on user behaviour.<ref name="sklearn-testimonials"/>

=== Technology ===

* '''AWeber''' uses scikit-learn to extract features from emails and build pipelines for managing large-scale email campaigns.<ref name="sklearn-testimonials"/>

* '''Solido''' applies it to semiconductor design tasks such as rare-event estimation and worst-case verification using statistical learning.<ref name="sklearn-testimonials"/>

* '''Evernote''', '''Dataiku''', and other tech companies employ scikit-learn in prototyping and production workflows due to its consistent API and integration with the Python ecosystem.<ref name="sklearn-testimonials"/>

=== Academia ===

* '''Télécom ParisTech''' integrates scikit-learn in hands-on coursework and assignments as part of its machine learning curriculum.<ref name="sklearn-testimonials"/>

== Awards ==

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* 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize<ref>{{Cite web |title=The 2019 Inria-French Academy of Sciences-Dassault Systèmes Innovation Prize : scikit-learn , a success story for machine learning free software {{!}} Inria |url=https://www.inria.fr/en/2019-inria-french-academy-sciences-dassault-systemes-innovation-prize-scikit-learn-success-story |access-date=2025-03-19 |website=www.inria.fr}}</ref>

* 2022 Open Science Award for Open Source Research Software<ref>{{Cite web |last=Badolato |first=Anne-Marie |date=2022-02-07 |title=Open Science Awards for Open Source Research Software |url=https://www.ouvrirlascience.fr/open-science-free-software-award-ceremony/ |access-date=2025-03-19 |website=Ouvrir la Science |language=en}}</ref>

==scikit-learn alternatives==

* [[mlpy]]

* [[SpaCy]]

* [[Natural Language Toolkit|NLTK]]

* [[Orange (software)|Orange]]

* [[PyTorch]]

* [[TensorFlow]]

* [[JAX (software)|JAX]]

* [[Infer.NET]]

* [[List of numerical analysis software]]

==References==

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[[Category:Python (programming language) scientific libraries]]

[[Category:Software using the BSD license]]

[[Category:2010 in artificial intelligence]]

[[Category:2010 software]]