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UID:pretalx-pyconde-pydata-2026-8YTYEN@pretalx.com
DTSTART;TZID=CET:20260415T165500
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DESCRIPTION:Many machine learning tools assume abundant\, independent data\
 , rely on a single data split plus cross-validation\, and leave test-set s
 eparation to the user.\n\nIn application-driven domains such as industrial
  materials science and pharmaceutical development\, data are scarce\, high
 -dimensional\, and often correlated\, creating conditions under which stan
 dard ML pipelines frequently fail. Small datasets are highly sensitive to 
 the random seed used for splitting\, and common pitfalls such as feature s
 election before splitting or distributing correlated samples across train 
 and test sets cause data leakage and inflated performance metrics.\n\nOcto
 pus is an open-source Python AutoML library explicitly designed for small-
 data\, high-dimensional regime. It enforces strict nested cross-validation
  for model and hyperparameter selection\, quantifies performance variabili
 ty across multiple splits\, and tightly controls data leakage. Its modular
  architecture embeds an internal ML engine\, several feature selection met
 hods (e.g.\, MRMR\, Boruta)\, and external AutoML solutions such as AutoGl
 uon into a unified\, rigorous validation framework\, enabling systematic a
 nd fair comparison of methods on limited data. In addition\, Octopus suppo
 rts survival analysis\, addressing time-to-event problems common in health
 care and materials science. This talk will use realistic small-scale datas
 ets to illustrate how conventional pipelines can be misleading and how to 
 obtain more reliable models when every sample matters.
DTSTAMP:20260412T141742Z
LOCATION:Europium [3rd Floor]
SUMMARY:Octopus AutoML: Extracting Signal from Small and High-Dimensional D
 ata - Nils Haase\, Andreas Wurl
URL:https://pretalx.com/pyconde-pydata-2026/talk/8YTYEN/
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