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UID:pretalx-euroscipy-2026-MLEJZS@pretalx.com
DTSTART;TZID=CET:20260721T093000
DTEND;TZID=CET:20260721T100000
DESCRIPTION:Skrub is a package that eases preparing dataframes so they can 
 be used in machine-learning tasks. In practice\, data can be spread over m
 ultiple tables\, represent various types of information (tabular\, textual
 \, graphical)\, or be stored on external database systems rather than data
 frames. \n\nSkrub Data Ops help with constructing versatile pipelines that
  can handle this variety of scenarios\, while at the same time avoiding da
 ta leakage and allowing to build rich hyper-parameter grids that can be ex
 plored to maximize the performance of the final machine learning model. \n
 \nIn this talk\, we give a brief introduction of the Data Ops framework be
 fore presenting three separate use cases highlighting their versatility: a
  traditional machine learning pipeline that uses Optuna to perform hyper-p
 arameter tuning\, a pipeline that trains on data stored in a relational da
 tabase rather than a dataframe\, and an image classification task with Pyt
 orch. \n\nBy the end of the talk\, attendees will learn about the skrub Da
 ta Ops\,  their main features and how they can be used successfully in dif
 ferent practical scenarios.
DTSTAMP:20260603T191438Z
LOCATION:Room 1.38 (Ground Floor\, Turing)
SUMMARY:How to use skrub Data Ops in practice - Riccardo Cappuzzo\, Guillau
 me Lemaitre
URL:https://pretalx.com/euroscipy-2026/talk/MLEJZS/
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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:20260603T191438Z
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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