37 citations · 57 across the 8 of their papers we have counts for
9 papers
Equally Good, Yet Different: Benchmarking Rashomon sets in AutoML packages
Katarzyna Woźnica, Katarzyna Rogalska, Zuzanna Sieńko +1
The Rashomon effect describes the existence of multiple near-optimal models that achieve comparable performance while offering fundamentally different explanations. This creates a…
Analyzing the Temporal Factors for Anxiety and Depression Symptoms with the Rashomon Perspective
Mustafa Cavus, Przemysław Biecek, Julian Tejada +2
This paper introduces a new modeling perspective in the public mental health domain to provide a robust interpretation of the relations between anxiety and depression, and the demo…
Beyond the Single-Best Model: Rashomon Partial Dependence Profile for Trustworthy Explanations in AutoML
Mustafa Cavus, Jan N. van Rijn, Przemysław Biecek
Automated machine learning systems efficiently streamline model selection but often focus on a single best-performing model, overlooking explanation uncertainty, an essential conce…
The Role of Hyperparameters in Predictive Multiplicity
Mustafa Cavus, Katarzyna Woźnica, Przemysław Biecek
This paper investigates the critical role of hyperparameters in predictive multiplicity, where different machine learning models trained on the same dataset yield divergent predict…
Investigating the Impact of Balancing, Filtering, and Complexity on Predictive Multiplicity: A Data-Centric Perspective
Mustafa Cavus, Przemyslaw Biecek
The Rashomon effect presents a significant challenge in model selection. It occurs when multiple models achieve similar performance on a dataset but produce different predictions,…
An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classification
Mustafa Cavus, Przemysław Biecek
Predictive models may generate biased predictions when classifying imbalanced datasets. This happens when the model favors the majority class, leading to low performance in accurat…
Glocal Explanations of Expected Goal Models in Soccer
Mustafa Cavus, Adrian Stando, Przemyslaw Biecek
The expected goal models have gained popularity, but their interpretability is often limited, especially when trained using black-box methods. Explainable artificial intelligence t…
The Effect of Balancing Methods on Model Behavior in Imbalanced Classification Problems
Adrian Stando, Mustafa Cavus, Przemysław Biecek
Imbalanced data poses a significant challenge in classification as model performance is affected by insufficient learning from minority classes. Balancing methods are often used to…
Explainable expected goal models for performance analysis in football analytics
Mustafa Cavus, Przemysław Biecek
The expected goal provides a more representative measure of the team and player performance which also suit the low-scoring nature of football instead of score in modern football.…