Local Point-wise Explanations of LambdaMART

Abstract

LambdaMART has been shown to outperform neural network models on tabular Learning-to-Rank (LTR) tasks. Similar to the neural network models, LambdaMART is considered a black-box model due to the complexity of the logic behind its predictions. Explanation techniques can help us understand these models. Our study investigates the faithfulness of point-wise explanation techniques when explaining LambdaMART models. Our analysis includes LTR-specific explanation techniques, such as LIRME and EXS, as well as explanation techniques that are not adapted to LTR use cases, such as LIME, KernelSHAP, and LPI. The explanation techniques are evaluated using several measures: Consistency, Fidelity, (In)fidelity, Validity, Completeness, and Feature Frequency (FF) Similarity. Three LTR benchmark datasets are used in the investigation: LETOR 4 (MQ2008), Microsoft Bing Search (MSLR-WEB10K), and Yahoo! LTR challenge dataset. Our empirical results demonstrate the challenges of accurately explaining LambdaMART: no single explanation technique is consistently faithful across all our evaluation measures and datasets. Furthermore, our results show that LTR-based explanation techniques are not consistently better than their non-LTR-based counterparts across the evaluation measures. Specifically, the LTR-based explanation techniques consistently are the most faithful with respect to (In)fidelity, whereas the non-LTR-specific approaches are shown to frequently provide the most faithful explanations with respect to Validity, Completeness, and FF Similarity.

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