Temel Kavramlar
Effector is a Python library that provides global and regional feature effect methods to explain the behavior of black-box machine learning models. It implements well-established global effect methods like Partial Dependence Plots (PDP) and Accumulated Local Effects (ALE), and their regional counterparts, which can uncover heterogeneous feature effects hidden behind global averages.
Özet
The content introduces Effector, a Python library dedicated to regional explainability methods for machine learning models.
Key highlights:
Global feature effect methods like PDP and ALE explain a model by showing the average effect of each feature on the output. However, these average effects can be misleading when there are significant interactions between features, leading to heterogeneous local effects.
Regional effect methods partition the input space into subspaces and compute explanations within each subspace, reducing the impact of feature interactions and aggregation bias.
Effector implements well-established global effect methods and their regional counterparts, providing a consistent API and enabling easy integration of new methods.
The library is designed to be extensible, allowing researchers to develop and benchmark novel regional effect methods.
Effector provides comprehensive tutorials and documentation to familiarize the community with regional explainability techniques.
The library is available on PyPI and GitHub under an open-source license.
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