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SUMMARY:Scikit-learn's Metadata Routing API - Stefanie Senger
DTSTART;TZID=Poland:20260720T143000
DTEND;TZID=Poland:20260720T150000
DTSTAMP:20260915T033225Z
UID:pretalx-euroscipy-2026-VXRSM8@pretalx.com
DESCRIPTION:This talk will introduce scikit-learn users to the new API for
  metadata routing\, a feature introduced in the recent releases available 
 in experimental mode. As a scikit-learn core developer\, I'll share insigh
 ts from my experience working directly on this feature.\n\nWe will explore
  what metadata is\, how it can be used in machine learning pipelines\, and
  how the new API simplifies routing metadata throughout a workflow. Routin
 g metadata refers to an internal mechanism to pass metadata around between
  components of a data science pipeline\, ensuring it reaches the functions
  that consume or utilize it.\n\nUsing well-known metadata such as sample_w
 eight and groups which are implemented in many scikit-learn metrics and ev
 aluation tools\, we will examine the restrictions for passing metadata pri
 or to the introduction of the new API. Then\, we will enable the new routi
 ng API and demonstrate how it solves these challenges with examples that i
 nvolve layers of nested-ness through cross-validation\, hyperparameter tun
 ing\, or pipelines. We will explain the core components of the API\, inclu
 ding methods like set_fit_request() and how to actually pass our metadata.
 \n\nAttendees will leave with an understanding of how to enable and use th
 e new routing API including passing metadata through Pipeline objects and 
 validation tools like cross_validate. Additional references to the metadat
 a user guide and developer guide will be provided for those interested in 
 further exploration.
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/VXRSM8/
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