Albert Dorador: From black to white boxes: Interpretable regression with the trust-free Python package

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 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.