Albert Dorador
Albert Dorador is an Adjunct Professor of Mathematics (Universitat Pompeu Fabra) and Statistics (BarcelonaTech), and leads a Research Lab focused on the development of cutting edge, inherently interpretable machine learning models for tabular data (Whitebox Lab). He holds a PhD in Statistics from the University of Wisconsin–Madison and previously served at the European Central Bank, specializing in financial risk management and machine learning applications. Albert is the creator of the TRUST and Renet algorithms and the maintainer of the trust-free Python library. His work focuses on the intersection of high-performance statisical modeling and auditable machine learning for high-stakes regulatory environments.
BarcelonaTech
Professor
AlbertDC90
Session
Machine Learning practitioners often face a trade-off: high accuracy with complex, black-box models (like XGBoost or Random Forests) or lower accuracy with transparent models (like decision trees or linear models). What if you didn't have to choose?
This 90-minute tutorial introduces TRUST (Transparent, Robust, and Ultra-Sparse Trees), a new interpretable regression framework that combines decision 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.
By the end, you will be able to train high-performing, interpretable regression models and generate automated, natural-language explanation reports for individual predictions and deterministic feature importance.