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DTSTART:20251026T030000
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SUMMARY:Deal with imbalanced classification using scikit-learn - Guillaume
  Lemaitre\, Anne Beyer
DTSTART;TZID=Poland:20260722T090000
DTEND;TZID=Poland:20260722T103000
DTSTAMP:20260913T105124Z
UID:pretalx-euroscipy-2026-D9FJAC@pretalx.com
DESCRIPTION:Class imbalance is a common challenge in real-world machine le
 arning. This course explores why standard approaches fail and how to build
  reliable classifiers using scikit-learn's calibration and threshold-tunin
 g tools.\n\nWe cover practical solutions including resampling strategies\,
  probabilistic calibration with `CalibratedClassifierCV`\, and decision th
 reshold optimization using `TunedThresholdClassifierCV`. You'll learn to e
 valuate models appropriately with calibration curves and confusion matrice
 s.\n\nThe course also addresses prevalence shift or in other words when yo
 ur training data doesn't reflect the target population. We demonstrate wei
 ght-based training corrections and post-hoc probability adjustments applic
 able to any binary classifier.\n\nThe material is available here: https://
 github.com/probabl-ai/calibration-cost-sensitive-learning
LOCATION:Room 1.38 (Ground Floor\, Turing)
URL:https://pretalx.com/euroscipy-2026/talk/D9FJAC/
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