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UID:pretalx-euroscipy-2026-D9FJAC@pretalx.com
DTSTART;TZID=CET:20260722T110000
DTEND;TZID=CET:20260722T123000
DESCRIPTION:Class imbalance is a common challenge in real-world machine lea
 rning. This course explores why standard approaches fail and how to build 
 reliable classifiers using scikit-learn's calibration and threshold-tuning
  tools.\n\nWe cover practical solutions including resampling strategies\, 
 probabilistic calibration with `CalibratedClassifierCV`\, and decision thr
 eshold optimization using `TunedThresholdClassifierCV`. You'll learn to ev
 aluate models appropriately with calibration curves and confusion matrices
 .\n\nThe course also addresses prevalence shift or in other words when you
 r training data doesn't reflect the target population. We demonstrate weig
 ht-based training corrections and post-hoc probability adjustments applica
 ble to any binary classifier.
DTSTAMP:20260603T201709Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:Deal with imbalanced classification using scikit-learn - Guillaume 
 Lemaitre\, Anne Beyer
URL:https://pretalx.com/euroscipy-2026/talk/D9FJAC/
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