44 citations · 91 across the 6 of their papers we have counts for
4 papers · 1 filter
FOCUS: Flexible Optimizable Counterfactual Explanations for Tree Ensembles
Ana Lucic, Harrie Oosterhuis, Hinda Haned +1
Model interpretability has become an important problem in machine learning (ML) due to the increased effect that algorithmic decisions have on humans. Counterfactual explanations c…
Why Does My Model Fail? Contrastive Local Explanations for Retail Forecasting
Ana Lucic, Hinda Haned, Maarten de Rijke
In various business settings, there is an interest in using more complex machine learning techniques for sales forecasting. It is difficult to convince analysts, along with their s…
Global Aggregations of Local Explanations for Black Box models
Ilse van der Linden, Hinda Haned, Evangelos Kanoulas
The decision-making process of many state-of-the-art machine learning models is inherently inscrutable to the extent that it is impossible for a human to interpret the model direct…
Explaining Predictions from Tree-based Boosting Ensembles
Ana Lucic, Hinda Haned, Maarten de Rijke
Understanding how "black-box" models arrive at their predictions has sparked significant interest from both within and outside the AI community. Our work focuses on doing this by g…