[et_pb_section fb_built=”1″ admin_label=”section” _builder_version=”3.22″][et_pb_row _builder_version=”3.22.1″ border_color_all=”#3255c9″ border_style_all=”groove”][et_pb_column type=”1_4″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ min_height=”11px” custom_margin=”||-25px|||” custom_padding=”||0px|||”]<\/p>\n
Foula Vagena<\/strong> <\/p>\n [\/et_pb_text][\/et_pb_column][et_pb_column type=”3_4″ _builder_version=”3.22.1″][et_pb_text _builder_version=”3.22.1″ custom_margin=”|||||” custom_padding=”0px|||||”]<\/p>\n Ensemble learning is the process by which multiple models, such as classifiers or experts, are combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of a poor one. By strategically combining multiple models one can produce a new predictive model with reduced variance, bias and improved predictions. In this tutorial we will explain the bias-variance tradeoff and describe how popular ensemble techniques (such as bagging, boosting, stacking etc) handle it. We will conclude the tutorial with an illustrative prediction task using various ensemble models.<\/p>\n The Hands-On Workshop will focus on examples of ensemble models.<\/p>\n References on the Booster subject as recommended during the seminar:<\/p>\n <\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.22.1″][et_pb_divider color=”#3255c9″ admin_label=”Divider” _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text _builder_version=”3.22.1″ custom_padding=”0px|||||”]<\/p>\n Dr Foula Vagena [\/et_pb_text][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][et_pb_video_slider _builder_version=”3.22.1″ custom_margin=”10px||”][et_pb_video_slider_item src=”https:\/\/www.youtube.com\/watch?v=Vul-8NwaXro” src_webm=”https:\/\/www.youtube.com\/watch?v=Vul-8NwaXro” _builder_version=”3.22.1″ show_image_overlay=”off”][\/et_pb_video_slider_item][\/et_pb_video_slider][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.22.1″][et_pb_button button_url=”https:\/\/drive.google.com\/file\/d\/1gWJprgUBHX-_o2EqHsDqLq9b-b1CUbg-\/view” button_text=”Example code” button_alignment=”left” _builder_version=”3.22.1″ custom_button=”on” button_text_color=”#ffffff” button_bg_color=”#072c72″ button_border_width=”2px” button_border_color=”#072c72″ button_border_radius=”26″ button_icon=”%%20%%” button_icon_color=”#ffffff” button_on_hover=”off” background_layout=”dark” custom_margin=”10px|||” z_index_tablet=”500″ custom_css_after=”margin-left: 0!important;||” saved_tabs=”all” locked=”off” background_layout__hover_enabled=”on” background_layout__hover=”light” button_bg_color__hover_enabled=”on” button_bg_color__hover=”#ffffff” button_icon_color__hover_enabled=”on” button_icon_color__hover=”#072c72″ button_text_color__hover_enabled=”on” button_text_color__hover=”#072c72″ custom_css_after__hover_enabled=”on” custom_css_after__hover=”margin-left: 0!important;” button_border_color__hover_enabled=”on” button_border_color__hover=”#072c72″] [\/et_pb_text][et_pb_blog posts_number=”4″ include_categories=”67″ show_author=”off” show_date=”off” show_pagination=”off” module_id=”page_type_blog” _builder_version=”3.22.1″ header_level=”h4″ border_width_bottom_fullwidth=”1px” border_color_bottom_fullwidth=”rgba(51,51,51,0.18)” custom_padding=”||50px|” z_index_tablet=”500″][\/et_pb_blog][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":" Foula VagenaDecember 15, 4pmonline (zoom) Abstract Ensemble learning is the process by which multiple models, such as classifiers or experts, are combined to solve a particular computational intelligence problem. Ensemble learning is primarily used to improve the (classification, prediction, function approximation, etc.) performance of a model, or reduce the likelihood of an unfortunate selection of… Lire la suite Foula Vagena – Ensemble Learning: Theory and Techniques<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":2957,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"categories":[57,1,67],"tags":[],"class_list":["post-2951","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-57","category-diip","category-seminars-hands-on-workshops"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2951","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=2951"}],"version-history":[{"count":7,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2951\/revisions"}],"predecessor-version":[{"id":3165,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/posts\/2951\/revisions\/3165"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media\/2957"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=2951"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/categories?post=2951"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/tags?post=2951"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
December 15, 4pm
online (zoom)<\/span><\/p>\nAbstract<\/strong><\/h3>\n
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(Universit\u00e9 Paris Cit\u00e9, diiP)
Zografoula Vagena is a research associate at the Data Intelligence Institute of Paris (diiP) and affiliated with the Universit\u00e9 Paris Cit\u00e9. She has been a data science researcher and practitioner for over ten years. She has worked on different analytics problems including forecasting, image processing, graph analytics, multidimensional data analysis, text processing, recommendation systems, sequential data analysis and optimization within various fields such as transportation, healthcare, retail, finance\/insurance and accounting. She has also performed research in the intersection of data management and analytics, and was a primary contributor of the MCDB\/SimSQL systems that blended data management with Bayesian statistics. She holds a PhD in data management from the University of California, Riverside.
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\n[\/et_pb_button][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.22.1″][\/et_pb_column][et_pb_column type=”1_2″ _builder_version=”3.22.1″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.22.1″][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”3.22.1″][et_pb_row custom_margin=”120px||” _builder_version=”3.22.1″ locked=”off”][et_pb_column type=”4_4″ _builder_version=”3.0.47″][et_pb_divider _builder_version=”3.22.1″][\/et_pb_divider][et_pb_text admin_label=”\u00c0 lire aussi” _builder_version=”3.22.1″ z_index_tablet=”500″]<\/p>\nOther seminars
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