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 carefully designed Bayesian priors gives SMARTboost an edge (in comparison with a state-of-the-art tool like XGBoost) in many settings with continuous and mixed discrete-continuous features. Unlike other tree-based methods, it can also compute marginal effects.

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From the bus stop Albano walk across the bridge at the bus stop to campus Albano. On the other side of the bridge, use the entrance Greta Arwidssons väg 8, which is clearly signposted. Walk through to Lecture rooms 13 and 12. Lecture room 19 is on the other side of the skylight-covered atrium.