The authors focus on efficiently processing and analyzing content related to game-theoretic model explainers. They present the following key insights:
Marginal game values explain how the structure of a machine learning model utilizes the predictors, while conditional game values explain the model's output. The authors concentrate on marginal game values as they are important for financial industry regulations.
Directly computing marginal game values has high computational complexity, scaling exponentially with the number of predictors. The authors propose a Monte Carlo sampling approach to approximate these values efficiently.
The authors extend their sampling method to also approximate quotient game values, which explain the contributions of groups of predictors, and coalitional values, which provide individual predictor contributions within groups.
The authors provide a rigorous statistical analysis of their sampling algorithms, proving convergence and deriving error bounds. They show that the estimators are consistent and unbiased, with mean squared error converging at a rate of O(1/√K), where K is the number of samples.
Numerical experiments on synthetic data validate the theoretical findings, demonstrating the effectiveness of the proposed Monte Carlo sampling approach in approximating various game-theoretic explainers.
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by Konstandinos... alle arxiv.org 04-22-2024
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