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<!doctype html><html lang=en-us><head><meta name=generator content="Hugo 0.73.0"><script type=application/javascript>var doNotTrack=false;if(!doNotTrack){window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;ga('create','UA-169598487-2','auto');ga('send','pageview');}</script><script async src=https://www.google-analytics.com/analytics.js></script><meta property="og:title" content="scikit-multiflow"><meta property="og:description" content="Machine learning package for streaming data in Python"><meta property="og:type" content="website"><meta property="og:url" content="https://scikit-multiflow.github.io/"><meta property="og:updated_time" content="2020-06-17T00:00:00+00:00"><meta name=description content="Machine learning package for streaming data in Python"><meta charset=utf-8><meta name=viewport content="width=device-width,initial-scale=1,shrink-to-fit=no"><meta http-equiv=x-ua-compatible content="ie=edge"><title>scikit-multiflow</title><link rel=icon type=image/png href=/images/favicon.png><link href="https://fonts.googleapis.com/css?family=Lato:400,900" rel=stylesheet><link rel=stylesheet type=text/css href=/css/style.min.cf5efcb8a6e49b945af419cd8b208a5ae87eb3015995b9afc61218743a0fed3e.css integrity="sha256-z178uKbkm5Ra9BnNiyCKWuh+swFZlbmvxhIYdDoP7T4="><link rel=stylesheet type=text/css href=/css/icons.css><link href="https://fonts.googleapis.com/css?family=Lato:400,900&display=swap" rel=stylesheet><link href="https://fonts.googleapis.com/css?family=Source+Code+Pro&display=swap" rel=stylesheet><link rel=stylesheet type=text/css href=https://scikit-multiflow.github.io/css/custom_style.css><link rel=stylesheet type=text/css href=/css/content.css><link rel=stylesheet type=text/css href=/css/keyfeatures.css></head><body><div id=preloader><div id=status></div></div><nav id=nav class="navbar is-fresh is-transparent no-shadow" role=navigation aria-label="main navigation"><div class=container><div class=navbar-brand><a role=button class=navbar-burger aria-label=menu aria-expanded=false data-target=navbar-menu><span aria-hidden=true></span><span aria-hidden=true></span><span aria-hidden=true></span></a></div><div id=navbar-menu class="navbar-menu is-static"><div class=navbar-end><a href=https://scikit-multiflow.readthedocs.io/en/stable/installation.html id=navbar-item class="navbar-item is-secondary">Installation</a>
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<a href=/posts id=navbar-item class="navbar-item is-secondary">News</a></div></div></div></nav><div class=hero-container><div class=hero-content><div class=hero-title-content><div class=flex-column><div class=hero-title><img class=hero-logo src=/images/logos/skmultiflow-logo-wide.svg alt="scikit-multiflow logo."></div><div class=flex-column><div class=hero-subtitle>A machine learning package for streaming data in Python</div><div class=hero-cta><a href=https://github.com/scikit-multiflow/scikit-multiflow><button class=cta-button>GitHub repository</button></a></div></div></div></div></div></div><div class="hero-foot mb-20"><div class=container><div class="tabs is-centered"><ul></ul></div></div></div><section class=keyfeatures><div class=container><div class=keyfeatures-box-container><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Incremental learning</div><div class=keyfeatures-box-text>Stream learning models are created incrementally and are updated continuously. They are suitable for big data applications where real-time response is vital.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Adaptive learning</div><div class=keyfeatures-box-text>Changes in data distribution harm learning. Adaptive methods are specifically designed to be robust to concept drift changes in dynamic environments.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Resource-wise efficient</div><div class=keyfeatures-box-text>Streaming techniques efficiently handle resources such as memory and processing time given the unbounded nature of data streams.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Easy to use</div><div class=keyfeatures-box-text>scikit-multiflow is designed for users with any experience level. Experiments are easy to design, setup, and run. Existing methods are easy to modify and extend.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Stream learning tools</div><div class=keyfeatures-box-text>In its current state, scikit-multiflow contains data generators, multi-output/multi-target stream learning methods, change detection methods, evaluation methods, and more.</div></p></div><div class="keyfeatures-box-content keyfeatures-underline"><p><div class=keyfeatures-box-title>Open source</div><div class=keyfeatures-box-text>Distributed under the <a href=https://github.com/scikit-multiflow/scikit-multiflow/blob/master/LICENSE>BSD 3-Clause</a>, scikit-multiflow is developed and maintained <a href=https://github.com/scikit-multiflow/scikit-multiflow>publicly on GitHub</a> by an active, diverse and growing <a href=/community>community</a>.</div></p></div></div></div></section><section class="section is-medium"><div class=container><div class=columns><div class="column is-centered-tablet-portrait"><h1 class="title section-title">Use cases</h1><h3 class="subtitle is-5 is-muted">Learning tasks supported in scikit-multiflow</h3><div class=divider></div></div><div class="column is-7 mt-60"><article class="media icon-box"><div class="media-content mt-50"><div class=content><p><span class=icon-box-title>Supervised learning</span>
<span class=icon-box-text>When working with labeled data. Depending on the target type can be either classification (discrete values) or regression (continuous values)</span></p></div></div></article><article class="media icon-box"><div class="media-content mt-50"><div class=content><p><span class=icon-box-title>Single/multi output</span>
<span class=icon-box-text>Single-output methods predict a single target-label (binary or multi-class) for classification or a single target-value for regression. Multi-output methods simultaneously predict multiple variables given an input.</span></p></div></div></article><article class="media icon-box"><div class="media-content mt-50"><div class=content><p><span class=icon-box-title>Concept drift detection</span>
<span class=icon-box-text>Changes in data distribution can harm learning. Drift detection methods are designed to rise an alarm in the presence of drift and are used alongside learning methods to improve their robustness against this phenomenon in evolving data streams.</span></p></div></div></article><article class="media icon-box"><div class="media-content mt-50"><div class=content><p><span class=icon-box-title>Unsupervised learning</span>
<span class=icon-box-text>When working with unlabeled data. For example, anomaly detection where the goal is the identification of rare events or samples which differ significantly from the majority of the data.</span></p></div></div></article></div></div></div></section><div class=container><div class="title-wrapper has-text-centered"><h2 class="title is-2">Monitor performance</h2><h3 class="subtitle is-5 is-muted">Prequential evaluation example</h3></div><div class="column is-10 is-offset-1"><div class=has-text-centered><img src=/images/example_classifier_plot.gif></div></div></div><section class="section section-feature-grey"><div class=container><h5 class="title is-5">Sponsors</h2><div class=sponsor-images><a href=https://www.telecom-paristech.fr/eng><img src=/images/logos/logo-telecom-paristech.png height=80 width=80 alt="Telecom ParisTech logo."></a>
<a href=https://www.polytechnique.edu/en><img src=/images/logos/logo-polytechnique.png height=65 width=65 alt="Ecole Polytechnique logo."></a>
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<a href=https://www.waikato.ac.nz/><img src=/images/logos/logo-waikato.png height=280 width=280 alt="University of Waikato logo."></a></div><style>.sponsor-images{display:flex;flex-direction:row;max-width:1000px;justify-content:center;margin:30px}.sponsor-images>a{height:75px;width:auto;padding:0 30px;margin:30px 0}@media only screen and (max-width:850px){.sponsor-images{flex-direction:column}.sponsor-images>img{height:auto;margin:30px 0;padding:0}}</style></div><div class=container><h5 class="title is-5">Collaborating institutions/groups</h2><div class=partner-images><img src=/images/logos/logo-icmc.png height=180 width=180 alt="ICMC logo"></div><style>.partner-images{display:flex;flex-direction:row;max-width:1000px;justify-content:center;margin:30px}.partner-images>a{height:75px;width:auto;padding:0 30px;margin:30px 0}@media only screen and (max-width:850px){.partner-images{flex-direction:column}.partner-images>img{height:auto;margin:30px 0;padding:0}}</style></div><div class=container><h5 class="title is-5">Citing</h5></div><div class=container><br>If you want to cite scikit-multiflow, please use the following <a href=http://jmlr.org/papers/v19/18-251.html>JMLR paper</a> (<a href=misc/skmultiflow.bib>bibtex</a>).<br><blockquote><p>Montiel, J., Read, J., Bifet, A., & Abdessalem, T. (2018). Scikit-multiflow: A multi-output streaming framework. The Journal of Machine Learning Research, 19(72):1−5.</p></blockquote></div></section><footer id=footer class=footer><div class=container><div id=footer-columns class=columns><div class=footer-logo-column><img id=footer-logo src=/images/logos/skmultiflow-logo.svg alt="scikit-multiflow logo"></div><div class=link-column><div class=footer-column><div class=footer-header></div><ul class=link-list><li class=link-list><a class=footer-link href=https://scikit-multiflow.readthedocs.io/en/stable/installation.html>Install</a></li><li class=link-list><a class=footer-link href=https://scikit-multiflow.readthedocs.io/en/stable/index.html>Documentation</a></li><li class=link-list><a class=footer-link href=https://scikit-multiflow.readthedocs.io/en/stable/user-guide/user-guide.html>User Guide</a></li><li class=link-list><a class=footer-link href=/about#citing>Citing</a></li></ul></div></div><div class=link-column><div class=footer-column><div class=footer-header></div><ul class=link-list><li class=link-list><a class=footer-link href=/community>Community</a></li><li class=link-list><a class=footer-link href=https://github.com/scikit-multiflow/scikit-multiflow/blob/master/CONTRIBUTING.md>Contribute</a></li><li class=link-list><a class=footer-link href=/about>About</a></li><li class=link-list><a class=footer-link href=/posts>News</a></li></ul></div></div><div class=footer-actions><nav class="level is-mobile"><div class=social-media-icons><a class=level-item href=https://github.com/scikit-multiflow/scikit-multiflow aria-label=https://github.com/scikit-multiflow/scikit-multiflow><span class=icon><i class="fa fa-github"></i></span></a><a class=level-item href=https://gitter.im/scikit-multiflow/community aria-label=https://gitter.im/scikit-multiflow/community><span class=icon><i class="fa fa-gitter"></i></span></a></div></nav></div></div><div class=container><div class=copyright style=font-size:80%>&copy; 2019-2020 scikit-multiflow. Landing page based on hugo-fresh and numpy.org.</div></div></div></footer><div id=backtotop><a href=#></a></div><div id=backtotop><a href=#></a></div><script src=https://cdnjs.cloudflare.com/ajax/libs/jquery/2.2.4/jquery.min.js></script><script src=https://unpkg.com/feather-icons></script><script src=/js/fresh.js></script><script src=/js/jquery.panelslider.min.js></script><script src=https://cdnjs.cloudflare.com/ajax/libs/modernizr/2.8.3/modernizr.min.js></script><script src=https://kit.fontawesome.com/26fe64c428.js crossorigin=anonymous></script><script src=/js/app.js></script></body></html>