  Machine learning (ML) research has yielded powerful tools for training
accurate prediction models despite complex multivariate associations (e.g.
interactions and heterogeneity). In fields such as medicine, improved
interpretability of ML modeling is required for knowledge discovery,
accountability, and fairness. Rule-based ML approaches such as Learning
Classifier Systems (LCSs) strike a balance between predictive performance and
interpretability in complex, noisy domains. This work introduces the LCS
Discovery and Visualization Environment (LCS-DIVE), an automated LCS model
interpretation pipeline for complex biomedical classification. LCS-DIVE
conducts modeling using a new scikit-learn implementation of ExSTraCS, an LCS
designed to overcome noise and scalability in biomedical data mining yielding
human readable IF:THEN rules as well as feature-tracking scores for each
training sample. LCS-DIVE leverages feature-tracking scores and/or rules to
automatically guide characterization of (1) feature importance (2) underlying
additive, epistatic, and/or heterogeneous patterns of association, and (3)
model-driven heterogeneous instance subgroups via clustering, visualization
generation, and cluster interrogation. LCS-DIVE was evaluated over a diverse
set of simulated genetic and benchmark datasets encoding a variety of complex
multivariate associations, demonstrating its ability to differentiate between
them and then applied to characterize associations within a real-world study of
pancreatic cancer.
