3 citations · 7 across the 3 of their papers we have counts for
7 papers
Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models
Przemysław Biecek, Marcin Chlebus, Janusz Gajda +5
Rapid development of advanced modelling techniques gives an opportunity to develop tools that are more and more accurate. However as usually, everything comes with a price and in t…
Landscape of R packages for eXplainable Artificial Intelligence
Szymon Maksymiuk, Alicja Gosiewska, Przemyslaw Biecek
The growing availability of data and computing power fuels the development of predictive models. In order to ensure the safe and effective functioning of such models, we need metho…
Lifting Interpretability-Performance Trade-off via Automated Feature Engineering
Alicja Gosiewska, Przemyslaw Biecek
Complex black-box predictive models may have high performance, but lack of interpretability causes problems like lack of trust, lack of stability, sensitivity to concept drift. On…
EPP: interpretable score of model predictive power
Alicja Gosiewska, Mateusz Bakala, Katarzyna Woznica +2
The most important part of model selection and hyperparameter tuning is the evaluation of model performance. The most popular measures, such as AUC, F1, ACC for binary classificati…
Do Not Trust Additive Explanations
Alicja Gosiewska, Przemyslaw Biecek
Explainable Artificial Intelligence (XAI)has received a great deal of attention recently. Explainability is being presented as a remedy for the distrust of complex and opaque model…
SAFE ML: Surrogate Assisted Feature Extraction for Model Learning
Alicja Gosiewska, Aleksandra Gacek, Piotr Lubon +1
Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, inte…