Projects: pypi: scikit-learn
https://packages.ecosyste.ms/registries/pypi.org/packages/scikit-learn
A set of python modules for machine learning and data mining
72 versions
Latest release: almost 3 years ago
5,918 dependent packages
43,777,417 downloads last month
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a.joly@ulg.ac.be
vanderplas@astro.washington.edu
bh00038@cvplws63.eps.surrey.ac.uk
ha258@cornell.edu
satra@mit.edu
zl2480@columbia.edu
pinto@alum.mit.edu
jz4721@nyu.edu
matteo.visconti.gr@dartmouth.edu
vighneshbirodkar@nyu.edu
robert.l.marchman@dartmouth.edu
rs2715@stern.nyu.edu
claire.savard@colorado.edu
aaaagrawal@iitb.ac.in
ihaque@cs.stanford.edu
rspeer@mit.edu
mluessi@nmr.mgh.harvard.edu
amitibo@tx.technion.ac.il
ericchang2017@u.northwestern.edu
jkarno@seas.upenn.edu
minghui.liu@trincoll.edu
m.lyra@sussex.ac.uk
andrew.knyazev@ucdenver.edu
lamb@cs.stanford.edu
camilo@neurostat.mit.edu
martino.sorbaro@ed.ac.uk
t.sheerman-chase@surrey.ac.uk
cgohlke@uci.edu
batulaa@drexel.edu
kcarnold@alum.mit.edu
apw@seas.harvard.edu
abie@alum.mit.edu
rossbar@berkeley.edu
alceballosa@unal.edu.co
fhoang7@berkeley.edu
hechen@seas.upenn.edu
stefan@sun.ac.za
deepyaman.datta@utexas.edu
rebekah.kim@columbia.edu
shubham.bhardwaj2015@vit.ac.in
slama@berkeley.edu
nisingh@wharton.upenn.edu
kanikas3@vt.edu
jdlabal@stanford.edu
kjells@uw.edu
markus.loning.17@ucl.ac.uk
k_nzw@klis.tsukuba.ac.jp
dkirkby@uci.edu
balawson@bu.edu
c.brummitt@columbia.edu
choo8@illinois.edu
giorgio.patrini@anu.edu.au
shangwuy@andrew.cmu.edu
rafael.possas@sydney.edu.au
olsonran@msu.edu
brian.mcfee@nyu.edu
naj273@nyu.edu
dgrusak@trinity.edu
emd222@cornell.edu
voth0@sewanee.edu
trishnendu.dd2014@cs.iiests.ac.in
apeng@berkeley.edu
boucher@cs.umass.edu
lunt@ctbp.ucsd.edu
bakhbari@mgh.harvard.edu
piraka@brandeis.edu
neal.lathia@cl.cam.ac.uk
razhoshia@post.bgu.ac.il
michal.romaniuk06@ic.ac.uk
m.batchkarov@sussex.ac.uk
lemin@cs.colostate.edu
rudvlf0413@korea.ac.kr
vredev16@msu.edu
raschkas@msu.edu
ali.baharev@univie.ac.at
a.krasoulis@sms.ed.ac.uk
arokem@berkeley.edu
gabrielvr@al.insper.edu.br
eric.sanchez1234@email.bakersfieldcollege.edu
fcchou@stanford.edu
rofuyu@cs.utexas.edu
glemaitre@visor.udg.edu
naoyaiijima@hiroshima-u.ac.jp
ayushgup@iitk.ac.in
joel@vlan-2666-10-17-16-173.staff.wireless.sydney.edu.au
jam7w2@mail.missouri.edu
j.hale09@imperial.ac.uk
tw991@nyu.edu
patel_zeel@iitgn.ac.in
vincent.lostanlen@nyu.edu
stvjc@channing.harvard.edu
ritchieng@u.nus.edu
bansalsi@usc.edu
rbarnes@umn.edu
rrohan@cs.cmu.edu
liutong.zhou@columbia.edu
mdh386@nyu.edu
mlf419@nyu.edu
shreyave@usc.edu
jin.siy@northeastern.edu
samuel_ainsworth@brown.edu
cwijes1@lsu.edu
wadawson@ucdavis.edu
xinyuliu@umich.edu
rz258@cornell.edu
daniel.mallia21@myhunter.cuny.edu
argriffi@ncsu.edu
filipj@umich.edu
kms15@case.edu
sam.dixon@berkeley.edu
khong008@e.ntu.edu.sg
jnelso11@gmu.edu
jclawton@umich.edu
jaehyunahn@sogang.ac.kr
ijpulidos@unal.edu.co
gstupp@scripps.edu
justhube@umich.edu
noamkeidar@campus.technion.ac.il
nityamd@nyu.edu
css459@nyu.edu
alyee@ucsd.edu
andersk@mit.edu
dhanus@mit.edu
coreylevinson@uchicago.edu
cailean.carter@quadram.ac.uk
bral4884@colorado.edu
dschult@colgate.edu
dpwe@ee.columbia.edu
dylan.cashman@tufts.edu
pawelsendyk@berkeley.edu
qzhang90@gatech.edu
mhaberla@calpoly.edu
saketk@student.unimelb.edu.au
eunjikim@dm.snu.ac.kr
erinrhof@uw.edu
ksivaman@purdue.edu
garmstrong@ucsd.edu
konstantin.shmelkov@polytechnique.edu
e175774@ie.u-ryukyu.ac.jp
hermidal@cs.umd.edu
jbearer@hmc.edu
jmarin@csail.mit.edu
Repository Activity:
Repository Owner:
scikit-learn (organization)
Repositories related to the scikit-learn Python machine learning library. Academic
Repositories related to the scikit-learn Python machine learning library. Academic
README Analysis:
Science Score: 100/100
Starting Score: 100 points
Bonuses:
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+2840
Educational commit emails
142 contributors with educational email addresses -
+20
Academic repository owner
Repository owned by academic institution -
+15
Institutional repository owner
Repository owned by research institution -
+6
Science terms in README
3 scientific terms found in README
Penalties:
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-10
PyPI ecosystem
General-purpose ecosystem
Very Likely Science (100)
Papers Mentioning scikit-learn 2,431
10.1371/journal.pone.0251553
Artificial intelligence for classifying uncertain images by humans in determining choroidal vascular running pattern and comparisons with automated classification between artificial intelligenceCited by: 1
Author(s): Shozo Sonoda, Hideki Shiihara, Hiroto Terasaki, Naoko Kakiuchi, Ryoh Funatsu, Masatoshi Tomita, Yuki Shinohara, Eisuke Uchino, Takuma Udagawa, Guangzhou An, Masahiro Akïba, Hideo Yokota, Taiji Sakamoto
Software Mentions: 1
Published: over 5 years ago
10.3389/fimmu.2019.02080
T-Cell Receptor Cognate Target Prediction Based on Paired α and β Chain Sequence and Structural CDR Loop SimilaritiesCited by: 37
Author(s): Esteban Lanzarotti, Paolo Marcatili, Morten Nielsen
Software Mentions: 1
Published: about 7 years ago
10.1371/journal.pone.0114391
Systematic Classification of Disease Severity for Evaluation of Expanded Carrier Screening PanelsCited by: 84
Author(s): Gabriel A. Lazarin, Felicia Hawthorne, Nicholas S. Collins, E Platt, E. Edward Evans, Imran S. Haque
Software Mentions: 1
Published: almost 12 years ago
10.1371/journal.pone.0167370
A Machine Learning Approach for Using the Postmortem Skin Microbiome to Estimate the Postmortem IntervalCited by: 84
Author(s): Hunter R. Johnson, Donovan Trinidad, Stephania Guzman, Zenab Khan, James V. Parziale, Jennifer M. DeBruyn, Nathan H. Lents
Software Mentions: 1
Published: almost 10 years ago
10.1371/journal.pone.0216493
Large-area, high-resolution characterisation and classification of damage mechanisms in dual-phase steel using deep learningCited by: 42
Author(s): Carl F. Kusche, Tom Reclik, Martina Freund, Talal Al‐Samman, U. Kerzel, Sandra Korte‐Kerzel
Software Mentions: 1
Published: over 7 years ago
10.1371/journal.pone.0185458
Predictors of all-cause mortality among 514,866 participants from the Korean National Health Screening CohortCited by: 14
Author(s): Cheol–Hee Ahn, Yunji Hwang, Sung Sup Park
Software Mentions: 1
Published: almost 9 years ago
10.1371/journal.pone.0171207
Data-driven system to predict academic grades and dropoutCited by: 73
Author(s): S Domínguez Rovira, Eloi Puertas, Laura Igual
Software Mentions: 1
Published: over 9 years ago
10.3389/fnint.2018.00054
The Varieties of the Psychedelic Experience: A Preliminary Study of the Association Between the Reported Subjective Effects and the Binding Affinity Profiles of Substituted Phenethylamines and TryptaminesCited by: 46
Author(s): Federico Zamberlán, Camila Sanz, Rocio Martínez Vivot, Carla Pallavicini, Fire Erowid, Earth Erowid, Enzo Tagliazucchi
Software Mentions: 1
Published: almost 8 years ago
10.3389/fmed.2019.00255
Open Practices and Resources for Collaborative Digital PathologyCited by: 10
Author(s): Raphaël Marée
Software Mentions: 1
Published: almost 7 years ago
10.3389/fmed.2021.658665
Analysis of the Impact of Medical Features and Risk Prediction of Acute Kidney Injury for Critical Patients Using Temporal Electronic Health Record Data With Attention-Based Neural NetworkCited by: 3
Author(s): Zhimeng Chen, Ming Chen, Xu Sun, Xieli Guo, Qiuna Li, Yinqiong Huang, Yuren Zhang, Lianwei Wu, Yu Liu, Jinting Xu, Yuming Fang, Xiahong Lin
Software Mentions: 1
Published: over 5 years ago
10.1371/journal.pone.0255748
Regional performance variation in external validation of four prediction models for severity of COVID-19 at hospital admission: An observational multi-centre cohort studyCited by: 2
Author(s): Kristin Wickstrøm, Valeria Vitelli, Ewan Carr, Aleksander Rygh Holten, Rebecca Bendayan, Andrew H. Reiner, Daniel Bean, Thomas Searle, Anthony Shek, Željko Kraljević, James Teo, Richard Dobson, Kristian Tonby, Alvaro Köhn‐Luque, E Amundsen
Software Mentions: 1
Published: about 5 years ago
10.1186/s12911-021-01591-x
Improvement of APACHE II score system for disease severity based on XGBoost algorithmCited by: 8
Author(s): Yan Luo, Zhiyu Wang, Cong Wang
Software Mentions: 1
Published: about 5 years ago
10.3389/fmed.2021.683431
Development and Validation of Predictors for the Survival of Patients With COVID-19 Based on Machine LearningCited by: 2
Author(s): Yongfeng Zhao, Qianjun Chen, Tao Liu, Ping Luo, Yi Zhou, Minghui Liu, Bei Xiong, Fuling Zhou
Software Mentions: 1
Published: about 5 years ago
10.3389/fmicb.2020.623788
Rapid Microbial Quality Assessment of Chicken Liver Inoculated or Not With Salmonella Using FTIR Spectroscopy and Machine LearningCited by: 9
Author(s): Dimitra Dourou, Αθηνά Γρούντα, Anthoula A. Argyri, George Froutis, Panagiοtis Tsakanikas, Agapi I. Doulgeraki, Nikos Chorianopoulos, Chrysoula C. Tassou
Software Mentions: 1
Published: over 5 years ago
10.1186/s12880-021-00660-x
Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankleCited by: 3
Author(s): Wen Yan, Xiangjun Meng, Jinglai Sun, Hui Yu, Zhi Wang
Software Mentions: 1
Published: about 5 years ago
10.3389/fmicb.2019.02582
Fourier-Transform Infrared (FTIR) Spectroscopy for Typing of Clinical Enterobacter cloacae Complex IsolatesCited by: 45
Author(s): Sophia Vogt, Kim Löffler, Ariane Dinkelacker, Birgit Bader, Ingo B. Autenrieth, Silke Peter, Jan Liese
Software Mentions: 1
Published: almost 7 years ago
10.18632/aging.102900
Prediction of chronological and biological age from laboratory dataCited by: 15
Author(s): Luke Sagers, Luke Melas-Kyriazi, Chirag Patel, Arjun K. Manrai
Software Mentions: 1
Published: over 6 years ago
10.1186/s12880-019-0392-7
MRI-based radiomics of rectal cancer: preoperative assessment of the pathological featuresCited by: 58
Author(s): Xiaolu Ma, Shoukuan Fu, Yan Jia, Yuwei Xia, Yongling Li, Jianping Lu
Software Mentions: 1
Published: almost 7 years ago
10.1371/journal.pone.0209738
‘A world of competing sorrows’: A mixed methods analysis of media reports of children with cancer abandoning conventional treatmentCited by: 4
Author(s): Caroline Diorio, Michael Afanasiev, Kristen Salena, Stacey Marjerrison
Software Mentions: 1
Published: almost 8 years ago
10.1371/journal.pone.0235663
Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s Disease Neuroimaging (ADNI) databaseCited by: 17
Author(s): Juan Felipe Beltrán, Brandon Malik Wahba, Nicole Hose, Dennis Shasha, Richard Kline
Software Mentions: 1
Published: about 6 years ago
10.1371/journal.pone.0253027
Automatic ladybird beetle detection using deep-learning modelsCited by: 8
Author(s): Pablo Venegas, Francisco Calderón, Daniel Riofrío, Diego S. Benítez, Giovanni Ramón, Diego F. Cisneros‐Heredia, Miguel Coimbra, José Luis Rojo-Álvarez, Noel Pérez
Software Mentions: 1
Published: over 5 years ago
10.3389/fmicb.2018.00476
PVP-SVM: Sequence-Based Prediction of Phage Virion Proteins Using a Support Vector MachineCited by: 151
Author(s): Balachandran Manavalan, Tae Hwan Shin, Gwang Lee
Software Mentions: 1
Published: over 8 years ago
10.1371/journal.pone.0229620
Evaluation of machine learning models for automatic detection of DNA double strand breaks after irradiation using a γH2AX foci assayCited by: 8
Author(s): Tim Hohmann, Jacqueline Keßler, Dirk Vordermark, Faramarz Dehghani
Software Mentions: 1
Published: over 6 years ago
10.3389/fmicb.2021.696921
Comparative Analysis of Machine Learning Algorithms on Surface Enhanced Raman Spectra of Clinical Staphylococcus SpeciesCited by: 31
Author(s): Jia-Wei Tang, Qinghua Liu, Xiao-Cong Yin, Ya-Cheng Pan, Pengbo Wen, Xin Liu, Xingxing Kang, Bing Gu, Zuobin Zhu, Liang Wang
Software Mentions: 1
Published: about 5 years ago
10.1186/s12880-021-00560-0
Evaluating treatment response to neoadjuvant chemoradiotherapy in rectal cancer using various MRI-based radiomics modelsCited by: 18
Author(s): Zhihui Li, Xiaolu Ma, Shoukuan Fu, Haidi Lu, Yuwei Xia, Jianping Lu
Software Mentions: 1
Published: over 5 years ago
10.3389/fmicb.2021.661132
Machine Learning for Predicting Mycotoxin Occurrence in MaizeCited by: 10
Author(s): Marco Camardo Leggieri, Marco Mazzoni, Paola Battilani
Software Mentions: 1
Published: over 5 years ago
10.1186/s12920-020-00779-w
TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddingsCited by: 7
Author(s): Trinh Trung Duong Nguyen, Nguyen Quoc Khanh Le, Quang‐Thai Ho, Dinh-Van Phan, Yu‐Yen Ou
Software Mentions: 1
Published: almost 6 years ago
10.1371/journal.pone.0206409
An open-source k-mer based machine learning tool for fast and accurate subtyping of HIV-1 genomesCited by: 63
Author(s): Stephen Solis-Reyes, Mariano Avino, Art F. Y. Poon, Lila Kari
Software Mentions: 1
Published: almost 8 years ago
10.1371/journal.pone.0237937
Man vs. machine: Predicting hospital bed demand from an emergency departmentCited by: 3
Author(s): Filipe Rissieri Lucini, Mateus Augusto dos Reis, Giovani J.C. da Silveira, Flávio Sanson Fogliatto, Michel J. Anzanello, Giordanna Guerra Andrioli, Rafael Nicolaidis, Rafael Coimbra Ferreira Beltrame, Jeruza Lavanholi Neyeloff, Beatriz D’Agord Schaan
Software Mentions: 1
Published: about 6 years ago
10.3389/fmolb.2019.00047
Machine Learning Classification Model for Functional Binding Modes of TEM-1 β-LactamaseCited by: 13
Author(s): Feng Wang, Li Shen, Haoming Zhou, Shouyi Wang, Xinlei Wang, Peng Tao
Software Mentions: 1
Published: about 7 years ago