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SUMMARY:Probabilistic classification and cost-sensitive learning with scik
 it-learn - Guillaume Lemaitre\, Olivier Grisel
DTSTART;TZID=Europe/Berlin:20240826T140000
DTEND;TZID=Europe/Berlin:20240826T153000
DTSTAMP:20260818T113009Z
UID:pretalx-euroscipy-2024-UNYV7V@pretalx.com
DESCRIPTION:Data scientists are repeatedly told that it is absolutely crit
 ical to align their model training methodology with a specific business ob
 jective. While being a rather good advice\, it usually falls short on deta
 ils on how to achieve this in practice.\n\nThis hands-on tutorial aims to 
 introduce helpful theoretical concepts and concrete software tools to help
  them bridge this gap. This method will be illustrated on a worked practic
 al use case: optimizing the operations of a fraud detection system for a p
 ayment processing platform.\n\nMore specifically\, we will introduce the c
 oncepts of calibrated probabilistic classifiers\, how to evaluate them and
  fix common causes of mis-calibration. In a second part\, we will explore 
 how to turn probabilistic classifiers into optimal business decision maker
 s.\n\nThe tutorial material is available at the following URL: https://git
 hub.com/probabl-ai/calibration-cost-sensitive-learning
LOCATION:Room 5
URL:https://pretalx.com/euroscipy-2024/talk/UNYV7V/
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