1 citations · 2 across the 4 of their papers we have counts for
4 papers
Interpretability-by-Design with Accurate Locally Additive Models and Conditional Feature Effects
Vasilis Gkolemis, Loukas Kavouras, Dimitrios Kyriakopoulos +5
Generalized additive models (GAMs) offer interpretability through independent univariate feature effects but underfit when interactions are present in data. GAMs add selected p…
Effector: A Python package for regional explanations
Vasilis Gkolemis, Christos Diou, Dimitris Kyriakopoulos +10
Effector is a Python package for interpreting machine learning (ML) models that are trained on tabular data through global and regional feature effects. Global effects, like Partia…
Regionally Additive Models: Explainable-by-design models minimizing feature interactions
Vasilis Gkolemis, Anargiros Tzerefos, Theodore Dalamagas +2
Generalized Additive Models (GAMs) are widely used explainable-by-design models in various applications. GAMs assume that the output can be represented as a sum of univariate funct…
RHALE: Robust and Heterogeneity-aware Accumulated Local Effects
Vasilis Gkolemis, Theodore Dalamagas, Eirini Ntoutsi +1
Accumulated Local Effects (ALE) is a widely-used explainability method for isolating the average effect of a feature on the output, because it handles cases with correlated feature…