  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
(https://xai-effector.github.io/) and is available under an open-source license
at PyPi (https://pypi.org/project/effector/) and Github
(https://github.com/givasile/effector).
