Traditional evaluation metrics for classification in natural language
processing such as accuracy and area under the curve fail to differentiate
between models with different predictive behaviors despite their similar
performance metrics. We introduce sensitivity score, a metric that scrutinizes
models' behaviors at the vocabulary level to provide insights into disparities
in their decision-making logic. We assess the sensitivity score on a set of
representative words in the test set using two classifiers trained for hospital
readmission classification with similar performance statistics. Our experiments
compare the decision-making logic of clinicians and classifiers based on rank
correlations of sensitivity scores. The results indicate that the language
model's sensitivity score aligns better with the professionals than the xgboost
classifier on tf-idf embeddings, which suggests that xgboost uses some spurious
features. Overall, this metric offers a novel perspective on assessing models'
robustness by quantifying their discrepancy with professional opinions. Our
code is available on GitHub (this https URL).

本文介绍了一种新的评估模型性能的方法 —— 敏感分数，并通过比较专业医生和机器学习模型敏感分数在词汇级别上的排名相关性来评估其决策逻辑差异。结果表明，敏感分数可以更准确地衡量模型的鲁棒性，该方法可以用于自然语言处理等领域的分类任务的性能评估。