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

Enhanced Analysis
Educational Contributors: 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
README Analysis:
Science Score: 100/100
Starting Score: 100 points
Bonuses:
  • +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:
  • -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 intelligence
Cited 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 Similarities
Cited 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 Panels
Cited 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 Interval
Cited 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 learning
Cited 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 Cohort
Cited 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 dropout
Cited 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 Tryptamines
Cited 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 Pathology
Cited 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 Network
Cited 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 study
Cited 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 algorithm
Cited 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 Learning
Cited 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 Learning
Cited 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 ankle
Cited 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 Isolates
Cited 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 data
Cited 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 features
Cited 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 treatment
Cited 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) database
Cited 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 models
Cited 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 Machine
Cited 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 assay
Cited 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 Species
Cited 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 models
Cited 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 Maize
Cited 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 embeddings
Cited 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 genomes
Cited 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 department
Cited 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-Lactamase
Cited by: 13
Author(s): Feng Wang, Li Shen, Haoming Zhou, Shouyi Wang, Xinlei Wang, Peng Tao
Software Mentions: 1
Published: about 7 years ago