GeoShapley: A Game Theory Approach to Measuring Spatial Effects in Machine Learning Models
This provides a method for researchers and practitioners to interpret spatial influences in models, though it is incremental as it extends the existing Shapley value framework.
The paper tackles the problem of measuring spatial effects in machine learning models by introducing GeoShapley, a game theory approach that quantifies location importance and synergies with other features, validated on simulated data and applied to house price modeling.
This paper introduces GeoShapley, a game theory approach to measuring spatial effects in machine learning models. GeoShapley extends the Nobel Prize-winning Shapley value framework in game theory by conceptualizing location as a player in a model prediction game, which enables the quantification of the importance of location and the synergies between location and other features in a model. GeoShapley is a model-agnostic approach and can be applied to statistical or black-box machine learning models in various structures. The interpretation of GeoShapley is directly linked with spatially varying coefficient models for explaining spatial effects and additive models for explaining non-spatial effects. Using simulated data, GeoShapley values are validated against known data-generating processes and are used for cross-comparison of seven statistical and machine learning models. An empirical example of house price modeling is used to illustrate GeoShapley's utility and interpretation with real world data. The method is available as an open-source Python package named geoshapley.