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SUMMARY:Predictive survival analysis with scikit-learn\, scikit-survival a
 nd lifelines - Olivier Grisel\, Vincent Maladiere
DTSTART;TZID=Europe/Zurich:20230814T153000
DTEND;TZID=Europe/Zurich:20230814T170000
DTSTAMP:20260816T195726Z
UID:pretalx-euroscipy-2023-WBSYCM@pretalx.com
DESCRIPTION:This tutorial will introduce how to train machine learning mod
 els for time-to-event prediction tasks (health care\, predictive maintenan
 ce\, marketing\, insurance...) without introducing a bias from censored tr
 aining (and evaluation) data.
LOCATION:Aula
URL:https://pretalx.com/euroscipy-2023/talk/WBSYCM/
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BEGIN:VEVENT
SUMMARY:Interoperability in the Scientific Python Ecosystem - Joris Van de
 n Bossche\, Tim Head\, Olivier Grisel\, Franck Charras\, Mridul Seth\, Seb
 astian Berg
DTSTART;TZID=Europe/Zurich:20230817T103000
DTEND;TZID=Europe/Zurich:20230817T120000
DTSTAMP:20260816T195726Z
UID:pretalx-euroscipy-2023-CB9WMH@pretalx.com
DESCRIPTION:This slot will cover the effort regarding interoperability in 
 the scientific Python ecosystem. Topics:\n\n- Using the Array API for arra
 y-producing and array-consuming libraries\n- DataFrame interchange and nam
 espace APIs\n- Apache Arrow: connecting and accelerating dataframe librari
 es across the PyData ecosystem\n- Entry Points: Enabling backends and plug
 ins for your libraries\n\n### Using the Array API for array-producing and 
 array-consuming libraries\n\nAlready using the Array API or wondering if y
 ou should in a project you maintain? Join this maintainer track session to
  share your experience and exchange knowledge and tips around building arr
 ay libraries that implement the standard or libraries that consume arrays.
 \n\n### DataFrame-agnostic code using the DataFrame API standard\n\nThe Da
 taFrame Standard provides you with a minimal\, strict\, and predictable AP
 I\, to write code that will work regardless of whether the caller uses pan
 das\, polars\, or some other library.\n\n### DataFrame Interchange protoco
 l and Apache Arrow\n\nThe DataFrame interchange protocol and Arrow C Data 
 interface are two ways to interchange data between dataframe libraries. Wh
 at are the challenges and requirements that maintainers encounter when int
 egrating this into consuming libraries?\n\n### Entry Points: Enabling back
 ends and plugins for your libraries\n\nIn this talk\, we will discuss how 
 NetworkX used entry points to enable more efficient computation backends t
 o plug into NetworkX
LOCATION:HS 119 - Maintainer track
URL:https://pretalx.com/euroscipy-2023/talk/CB9WMH/
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SUMMARY:Exploring GPU-powered backends for scikit-learn - Olivier Grisel\,
  Franck Charras
DTSTART;TZID=Europe/Zurich:20230817T153000
DTEND;TZID=Europe/Zurich:20230817T160000
DTSTAMP:20260816T195726Z
UID:pretalx-euroscipy-2023-UVBBQZ@pretalx.com
DESCRIPTION:Could scikit-learn future be GPU-powered ? This talk will disc
 uss the performance improvements that GPU computing could bring to existin
 g scikit-learn algorithms\, and will describe a plugin-based design that i
 s being foresighted to open-up scikit-learn compatibility to faster comput
 e backends\, with special concern for user-friendliness\, ease of installa
 tion\, and interoperability.
LOCATION:Aula
URL:https://pretalx.com/euroscipy-2023/talk/UVBBQZ/
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