Interpretability in the Context of Sequential Cost-Sensitive Feature Acquisition

Despite the popularity of complex machine learning models, domain experts often struggle to understand and are reluctant to trust them due to lack of intuition and explanation of their predictions. Moreover, these cannot be used in many real-world applications, where features are not readily available but acquired at a cost. To address the latter challenge, dynamic instance-wise joint feature selection and classification selects both the order and the number of features to individually classify each data instance when features are sequentially acquired one at a time. Herein, its model-based and post hoc interpretability is demonstrated validating its utility in high-stakes applications. As a case study, predicting the credit risk of an individual based on financial and other data is considered. Experimental results show that the proposed method is indeed interpretable without sacrificing prediction accuracy.