44 citations · 44 across the 6 of their papers we have counts for
6 papers
path_boost: A Python Package for Interpretable Graph-Level Prediction using Path-Based Gradient Boosting
Claudio Meggio, Johan Pensar, Riccardo De Bin
We present path_boost, a Python package for interpretable supervised learning on graph-structured input data. The package implements PathBoost, a gradient boosting algorithm that a…
Path-Based Gradient Boosting for Graph-Level Prediction
Claudio Meggio, Johan Pensar, Riccardo De Bin
We propose PathBoost, a gradient tree boosting method for graph-level classification and regression that learns discriminative path-based features directly from the input graph str…
Fractional Polynomials Models as Special Cases of Bayesian Generalized Nonlinear Models
Aliaksandr Hubin, Georg Heinze, Riccardo De Bin
We propose a framework for fitting fractional polynomials models as special cases of Bayesian Generalized Nonlinear Models, applying an adapted version of the Genetically Modified…
A Decision Support System for Safer Airplane Landings: Predicting Runway Conditions Using XGBoost and Explainable AI
Alise Danielle Midtfjord, Riccardo De Bin, Arne Bang Huseby
The presence of snow and ice on runway surfaces reduces the available tire-pavement friction needed for retardation and directional control and causes potential economic and safety…
Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions
Clara Bertinelli Salucci, Azzeddine Bakdi, Ingrid K. Glad +2
Longevity and safety of lithium-ion batteries are facilitated by efficient monitoring and adjustment of the battery operating conditions. Hence, it is crucial to implement fast and…
Influence of single observations on the choice of the penalty parameter in ridge regression
Kristoffer H. Hellton, Camilla Lingjærde, Riccardo De Bin
Penalized regression methods such as ridge regression heavily rely on the choice of a tuning or penalty parameter, which is often computed via cross-validation. Discrepancies in th…