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UID:pretalx-pydata-london-2026-M8TE3Q@pretalx.com
DTSTART;TZID=GMT:20260605T141000
DTEND;TZID=GMT:20260605T154000
DESCRIPTION:Most machine learning methods give you a prediction but not a m
 easure of how much to trust it. Bayesian Additive Regression Trees (BART) 
 combine the flexibility of tree ensembles (e.g. random forests\, boosting)
  with full uncertainty quantification—every prediction comes with a prob
 ability interval\, not just a point estimate. This hands-on tutorial intro
 duces BART through three applications: regression\, classification\, and s
 urvival analysis. Using `pymc-bart`\, participants will learn to fit flexi
 ble models that automatically capture non-linear relationships while provi
 ding honest uncertainty estimates. We emphasize practical interpretation t
 hroughout: visualizing predictions with uncertainty bands\, understanding 
 variable importance\, and interpreting model output.
DTSTAMP:20260602T224309Z
LOCATION:Grand Hall 1
SUMMARY:Flexible Statistical Modeling with Bayesian Additive Regression Tre
 es - Chris Fonnesbeck
URL:https://pretalx.com/pydata-london-2026/talk/M8TE3Q/
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