Title
Effector: A Python package for regional explanations
Abstract
Global feature effect methods explain a model outputting one plot per feature. The plot shows the average effect of the feature on the output, like the effect of age on the annual income. However, average effects may be misleading when derived from local effects that are heterogeneous, i.e., they significantly deviate from the average. To decrease the heterogeneity, regional effects provide multiple plots per feature, each representing the average effect within a specific subspace. For interpretability, subspaces are defined as hyperrectangles defined by a chain of logical rules, like age's effect on annual income separately for males and females and different levels of professional experience. We introduce Effector, a Python library dedicated to regional feature effects. Effector implements well-established global effect methods, assesses the heterogeneity of each method and, based on that, provides regional effects. Effector automatically detects subspaces where regional effects have reduced heterogeneity. All global and regional effect methods share a common API, facilitating comparisons between them. Moreover, the library's interface is extensible so new methods can be easily added and benchmarked. The library has been thoroughly tested, ships with many tutorials (this https URL) and is available under an open-source license at PyPi (this https URL) and Github (this https URL).
Bio
Gkolemis received his MSc in Data Science from the University of Edinburgh and his diploma in Electrical and Computer Engineering from AUTH. He is currently a PhD candidate at Harokopio University of Athens, specializing in Explainable AI, and a Research Associate at ATHENA Research Center. His research focuses on explainable-by-design models and interpretability techniques for black-box models.
Practical
The speaker is presenting online.
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