2 citations · 2 across the 8 of their papers we have counts for
5 papers · 1 filter
mlr3mbo: Bayesian Optimization in R
Marc Becker, Lennart Schneider, Martin Binder +2
We present mlr3mbo, a modular toolbox for Bayesian optimization in R. mlr3mbo supports single- and multi-objective optimization, multi-point proposals, batch and asynchronous paral…
mlr3torch: A Deep Learning Framework in R based on mlr3 and torch
Sebastian Fischer, Lukas Burk, Carson Zhang +2
Deep learning (DL) has become a cornerstone of modern machine learning (ML) praxis. We introduce the R package mlr3torch, which is an extensible DL framework for the mlr3 ecosystem…
Analyzing Error Sources in Global Feature Effect Estimation
Timo HeiÃ, Coco Bögel, Bernd Bischl +1
Global feature effects such as partial dependence (PD) and accumulated local effects (ALE) plots are widely used to interpret black-box models. However, they are only estimates of…
A Large-Scale Neutral Comparison Study of Survival Models on Low-Dimensional Data
Lukas Burk, John Zobolas, Bernd Bischl +3
This work presents the first large-scale neutral benchmark experiment focused on single-event, right-censored, low-dimensional survival data. Benchmark experiments are essential in…
Constructing Confidence Intervals for 'the' Generalization Error -- a Comprehensive Benchmark Study
Hannah Schulz-Kümpel, Sebastian Fischer, Roman Hornung +3
When assessing the quality of prediction models in machine learning, confidence intervals (CIs) for the generalization error, which measures predictive performance, are a crucial t…