A problem-agnostic approach to feature selection and analysis using SHAP

Feature selection is an effective data reduction technique. SHapley Additive exPlanations (SHAP) can be used to provide a feature importance ranking for models built with labeled or unlabeled data. Thus, one may use the SHAP feature importance ranking in a feature selection technique by selecting the k highest ranking features. Furthermore, this SHAP-based feature selection technique is applicable regardless of the availability of labels for data. We use the Kaggle Credit Card Fraud detection dataset to simulate three label availability scenarios. When no labeled data is available, unsupervised learners should be used. We explore feature selection for data reduction with Isolation Forest and SHAP for this…

• FA
  Florida Atlantic University
• UD
  Universidad del Atlántico