104 citations · 115 across the 13 of their papers we have counts for
3 papers · 1 filter
Evaluating machine learning models in non-standard settings: An overview and new findings
Roman Hornung, Malte Nalenz, Lennart Schneider +5
Estimating the generalization error (GE) of machine learning models is fundamental, with resampling methods being the most common approach. However, in non-standard settings, parti…
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Lennart Purucker, Lennart Schneider, Marie Anastacio +3
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe…
Multi-Objective Optimization of Performance and Interpretability of Tabular Supervised Machine Learning Models
Lennart Schneider, Bernd Bischl, Janek Thomas
We present a model-agnostic framework for jointly optimizing the predictive performance and interpretability of supervised machine learning models for tabular data. Interpretabilit…