Random Forest Model and Sample Explainer for Non-experts in Machine Learning – Two Case Studies

Abstract

Machine Learning (ML) is becoming an increasingly critical technology in many areas such as health, business but also in everyday applications of significant societal importance. However, the lack of explainability or ability of ML systems to offer explanation on how they work, which refers to the model (related to the whole data) and sample explainability (related to specific samples) poses significant challenges in their adoption, verification, and in ensuring the trust among users and general public. We present novel integrated Random Forest Model and Sample Explainer – RFEX. RFEX is specifically designed for important class of users who are non-ML experts but are often the domain experts and key decision makers. RFEX provides easy to analyze one-page Model and Sample explainability summaries in tabular format with wealth of explainability information including classification confidence, tradeoff between accuracy and features used, as well as ability to identify potential outlier samples and features. We demonstrate RFEX on two case studies: mortality prediction for COVID-19 patients from the data obtained from Huazhong University of Science and Technology, Wuhan, China, and classification of cell type clusters for human nervous system based on the data from J. Craig Venter Institute. We show that RFEX offers simple yet powerful means of explaining RF classification at model, sample and feature levels, as well as providing guidance for testing and developing explainable and cost-effective operational prediction models.