Mainstream machine learning approaches to predictive analytics consistently prove their ability to perform well using a variety of datasets, although the task of identifying an optimally-performing machine learning approach for any given dataset becomes much less intuitive. Methods such as ensemble and transformation modeling have been developed to improve upon individual base learners and datasets with large degrees of variance. Despite the increased generalizability and flexibility of ensemble approaches, the cost often involves sacrificing inference for predictive ability. This paper introduces an alternative approach to ensemble modeling, combining the predictive ability of an ensemble framework with localized model construction through the incorporation of cluster analysis as a pre-processing technique. The workflow not only outperforms independent base learners and comparative ensemble methods, but also preserves local inferential capability by manipulating cluster parameters and maintaining interpretable relative importance values and non-transformed coefficients for the overall consideration of variable importance. This paper demonstrates the ensemble technique on a dataset to estimate rates of health insurance coverage across the state of Missouri, where the cluster pre-processing assists in understanding both local and global variable importance and interactions when predicting high concentration areas of low health insurance coverage based on demographic, socioeconomic, and geospatial variables.

This paper examines Evidence-Based Decision Making (EBDM) within the context of ecological rationality. It contrasts classical rationality, which prioritizes comprehensive and logical evidence utilization, with ecological rationality, which emphasizes practical decision making (DM) under real-world constraints. Our examination underscores the importance of adaptive heuristics, professional judgment, and the integration of experience and expertise in forming intuitive responses. It also examines the limitations of framing intuitive versus analytical thinking as a strict dichotomy and advocates for a balanced approach that considers context and practical constraints. Finally, the paper addresses the potential impacts of motivated reasoning and bias in decision-making. Concluding with practical recommendations, it guides practitioners in applying EBDM in an ecologically rational way, stressing the need to balance an emphasis on classical rationality with professional judgment, expertise, and the specificities of each decision context.