Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths

• URL: http://arxiv.org/abs/2203.02295v1
• Date: Fri, 4 Mar 2022 13:11:31 GMT
• Title: Evaluating Local Model-Agnostic Explanations of Learning to Rank Models with Decision Paths
• Abstract summary: Local explanations of learning-to-rank models are thought to extract the most important features that contribute to the ranking predicted by the model for a single data point. We show that neither of the explanation techniques can achieve an acceptable explanation accuracy when the chosen similarity metric is AUC score or Spearman's rank correlation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Local explanations of learning-to-rank (LTR) models are thought to extract the most important features that contribute to the ranking predicted by the LTR model for a single data point. Evaluating the accuracy of such explanations is challenging since the ground truth feature importance scores are not available for most modern LTR models. In this work, we propose a systematic evaluation technique for explanations of LTR models. Instead of using black-box models, such as neural networks, we propose to focus on tree-based LTR models, from which we can extract the ground truth feature importance scores using decision paths. Once extracted, we can directly compare the ground truth feature importance scores to the feature importance scores generated with explanation techniques. We compare two recently proposed explanation techniques for LTR models and benchmark them using decision trees and gradient boosting models on the MQ2008 dataset. We show that neither of the explanation techniques can achieve an acceptable explanation accuracy when the chosen similarity metric is AUC score or Spearman's rank correlation.

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