Welcome to the next seminar within our traditional

Seminar series in Statistics and Data Science

Speaker: Paolo Giordani, Professor of Financial Econometrics, BI

Title:  SMARTboost for Tabular Data

When? TUESDAY,  15.03.2021, 14:15-15:15

Where?  Erling Svedrups plass and Zoom https://uio.zoom.us/j/68227870839?pwd=SDRobDVrNU9xUDVyM3Zvc0wyeUwrQT09

Abstract: 

We introduce SMARTboost (boosting of symmetric smooth additive regression trees), a machine learning model capable of fitting complex functions in high dimensions, yet designed for good performance in small n and low signal-to-noise environments. SMARTboost inherits many of the qualities that have made boosted trees the most widely used machine learning tool for tabular data; it automatically adjusts model complexity, handles continuous and discrete features, can capture nonlinear functions in high dimensions without overfitting, performs variable selection, and can handle highly non-Gaussian features. The combination of smooth symmetric trees and of carefully designed Bayesian priors gives SMARTboost an edge (in comparison with a state-of-the-art tool like XGBoost) in most settings with continuous and mixed discrete-continuous features. Unlike other tree-based methods, it can also compute marginal effects.

Best regards,

Sven Ove Samuelsen & Aliaksandr Hubin