We introduce the XPER (eXplainable PERformance) methodology to measure the specific contribution of the input features to the predictive or economic performance of a model. Our methodology offers several advantages. First, it is both model-agnostic and performance metric-agnostic. Second, XPER is theoretically founded as it is based on Shapley values. Third, the interpretation of the benchmark, which is inherent in any Shapley value decomposition, is meaningful in our context. Fourth, XPER is not plagued by model specification error, as it does not require re-estimating the model. Fifth, it can be implemented either at the model level or at the individual level. In an application based on auto loans, we find that performance can be explained by a surprisingly small number of features. XPER decompositions are rather stable across metrics, yet some feature contributions switch sign across metrics. Our analysis also shows that explaining model forecasts and model performance are two distinct tasks.
翻译：我们引入 XPER (可扩展性性能) 方法, 以衡量输入特征对模型预测或经济绩效的具体贡献。 我们的方法具有若干优点。 首先, 它既是模型的不可知性,又是性能的可计量性。 其次, XPER 的理论基础是光谱值。 第三, 对基准的解释( 任何光谱值分解中固有的), 对我们的背景都是有意义的。 第四, XPER 不受模型规格错误的困扰, 因为不需要重新估计模型。 第五, 它可以在模型一级或个人一级实施。 在基于自动贷款的应用程序中, 我们发现业绩可以用数量惊人的少的特性来解释。 XPER 的分解分布在不同的指标中相当稳定, 但是有些特征贡献转换符号跨度。 我们的分析还表明, 解释模型预测和模型性能是两种不同的任务。
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