2 citations · 3 across the 10 of their papers we have counts for
4 papers · 1 filter
Quantifying Visual Properties of GAM Shape Plots: Impact on Perceived Cognitive Load and Interpretability
Sven Kruschel, Lasse Bohlen, Julian Rosenberger +2
Generalized Additive Models (GAMs) offer a balance between performance and interpretability in machine learning. The interpretability aspect of GAMs is expressed through shape plot…
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…
A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
Sandra Zilker, Sven Weinzierl, Mathias Kraus +2
Proactive analysis of patient pathways helps healthcare providers anticipate treatment-related risks, identify outcomes, and allocate resources. Machine learning (ML) can leverage…
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…