论文标题
关于石灰和摇摆的偏见变化特征，高稀疏电影推荐说明任务
On the Bias-Variance Characteristics of LIME and SHAP in High Sparsity Movie Recommendation Explanation Tasks
论文作者
论文摘要
我们根据电影推荐任务评估了两种流行的本地解释性技术，即石灰和外形。我们发现，这两种方法的行为取决于数据集的稀疏性。在数据集的密集段中，石灰的表现要好，而在稀疏片段中，Shap的功能更好。我们将这种差异追溯到石灰和摇动基础估计量的不同偏差变化特征。我们发现，与石灰相比，SHAP在数据的稀疏段中表现出较低的方差。我们将这种较低的差异归因于Shap固有的完整性约束属性和石灰中缺少的属性。该约束是正规化器的，因此增加了Shap估计器的偏差，但会降低其差异，从而导致良好的偏见差异权衡，尤其是在高稀疏数据设置中。有了这种见解，我们将相同的约束引入石灰，并制定了一个新颖的局部解释框架，称为完整性约束的石灰（攀爬），比石灰优于石灰，速度比Shap更快。
We evaluate two popular local explainability techniques, LIME and SHAP, on a movie recommendation task. We discover that the two methods behave very differently depending on the sparsity of the data set. LIME does better than SHAP in dense segments of the data set and SHAP does better in sparse segments. We trace this difference to the differing bias-variance characteristics of the underlying estimators of LIME and SHAP. We find that SHAP exhibits lower variance in sparse segments of the data compared to LIME. We attribute this lower variance to the completeness constraint property inherent in SHAP and missing in LIME. This constraint acts as a regularizer and therefore increases the bias of the SHAP estimator but decreases its variance, leading to a favorable bias-variance trade-off especially in high sparsity data settings. With this insight, we introduce the same constraint into LIME and formulate a novel local explainabilty framework called Completeness-Constrained LIME (CLIMB) that is superior to LIME and much faster than SHAP.
