10 citations · 14 across the 5 of their papers we have counts for
5 papers
Unveiling Location-Specific Price Drivers: A Two-Stage Cluster Analysis for Interpretable House Price Predictions
Paul Gümmer, Julian Rosenberger, Mathias Kraus +2
House price valuation remains challenging due to localized market variations. Existing approaches often rely on black-box machine learning models, which lack interpretability, or s…
Challenging the Performance-Interpretability Trade-off: An Evaluation of Interpretable Machine Learning Models
Sven Kruschel, Nico Hambauer, Sven Weinzierl +3
Machine learning is permeating every conceivable domain to promote data-driven decision support. The focus is often on advanced black-box models due to their assumed performance ad…
IGANN Sparse: Bridging Sparsity and Interpretability with Non-linear Insight
Theodor Stoecker, Nico Hambauer, Patrick Zschech +1
Feature selection is a critical component in predictive analytics that significantly affects the prediction accuracy and interpretability of models. Intrinsic methods for feature s…
GAM(e) changer or not? An evaluation of interpretable machine learning models based on additive model constraints
Patrick Zschech, Sven Weinzierl, Nico Hambauer +2
The number of information systems (IS) studies dealing with explainable artificial intelligence (XAI) is currently exploding as the field demands more transparency about the intern…
A Light in the Dark: Deep Learning Practices for Industrial Computer Vision
Maximilian Harl, Marvin Herchenbach, Sven Kruschel +3
In recent years, large pre-trained deep neural networks (DNNs) have revolutionized the field of computer vision (CV). Although these DNNs have been shown to be very well suited for…