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SUMMARY:From Black to White Boxes: Interpretable Regression with the trust
 -free Python package - Albert Dorador
DTSTART;TZID=Poland:20260722T140000
DTEND;TZID=Poland:20260722T153000
DTSTAMP:20260907T151025Z
UID:pretalx-euroscipy-2026-FL89YS@pretalx.com
DESCRIPTION:Machine Learning practitioners often face a trade-off: high ac
 curacy with complex\, black-box models (like XGBoost or Random Forests) or
  lower accuracy with transparent models (like decision trees or linear mod
 els). **What if you didn't have to choose?**\nThis 90-minute tutorial intr
 oduces **TRUST** (**T**ransparent\, **R**obust\, and **U**ltra-**S**parse 
 **T**rees)\, a new interpretable regression framework that combines decisi
 on trees with sparse linear models to deliver Random Forest accuracy. The 
 algorithm is implemented in the Python package `trust-free` (available via
  pip install). We will demonstrate how TRUST autonomously recovers the WHO
  obesity threshold (BMI = 30) from raw data to inform medical risk pricing
 .\nBy the end\, you will be able to train high-performing\, interpretable 
 regression models and generate automated\, natural-language explanation re
 ports for individual predictions and deterministic feature importance.
LOCATION:Room 1.19 (Ground Floor\, Shannon)
URL:https://pretalx.com/euroscipy-2026/talk/FL89YS/
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