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UID:pretalx-pyconde-pydata-2026-BAXEXY@pretalx.com
DTSTART;TZID=CET:20260414T171000
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DESCRIPTION:### Teaching an LLM to Tune GBDT — and Beyond\n\nHyperparamet
 er optimization for gradient boosted tree models is a repetitive yet cogni
 tively demanding task. Practitioners must combine statistical intuition wi
 th detailed\, library-specific knowledge—often buried across hundreds of
  pages of documentation for tools such as XGBoost\, LightGBM\, or CatBoost
 . As models and configurations grow in complexity\, traditional approaches
  like grid search\, random search\, or even Bayesian optimization struggle
  to incorporate semantic understanding of model behavior.\n\nUsing LGBM as
  a concrete case study\, I demonstrate how MCP and skills-powered agents\,
  orchestrated in a structured workflow\, can analyze model behavior and pr
 opose targeted hyperparameter adjustments grounded in both theory and libr
 ary-specific constraints.
DTSTAMP:20260502T141903Z
LOCATION:Ferrum [2nd Floor]
SUMMARY:Agent-Based Hyperparameter Optimization for Gradient Boosted Trees 
 - Huijo Kim
URL:https://pretalx.com/pyconde-pydata-2026/talk/BAXEXY/
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