A Quantitative Evaluation of Explainable Ensemble Learning for Probabilistic Risk Estimation and Decision Reliability in Enterprise Analytics

Abstract

This study addresses the problem that increasingly sophisticated ensemble learning systems can improve predictive accuracy in enterprise analytics while simultaneously creating challenges related to model opacity, probability calibration, risk communication, and dependable human decision-making. The purpose of the research is to quantitatively evaluate how explainable ensemble-learning capabilities influence Probabilistic Risk Estimation Quality and Decision Reliability in enterprise analytics. A quantitative, cross-sectional, case-study-based design was adopted across enterprise environments using machine learning, predictive analytics, risk analytics, and AI-assisted decision-support systems. Using purposive sampling, data were collected from data scientists, machine-learning specialists, business and data analysts, risk analysts and managers, IT and analytics managers, and other enterprise analytics professionals. Of 340 questionnaires distributed, 318 were returned and 306 valid responses were retained, producing a 90.0% usable response rate. The key variables were Ensemble Predictive Performance, Model Explainability and Interpretability, Probability Calibration and Risk Confidence, Explainable Risk Communication, Probabilistic Risk Estimation Quality, and Decision Reliability. Data were analyzed using descriptive statistics, Cronbach’s alpha, KMO and Bartlett’s tests, Pearson correlation, and two-stage multiple regression modeling. Decision Reliability recorded M = 4.18, SD = 0.52, while Probabilistic Risk Estimation Quality achieved M = 4.14, SD = 0.54. Probability Calibration and Risk Confidence was the strongest predictor of risk-estimation quality, β = .36, p < .001, with the first model explaining 64.1% of variance, R² = .641. Probabilistic Risk Estimation Quality was the strongest predictor of Decision Reliability, β = .39, p < .001, while the second model explained 70.3% of variance, R² = .703, adjusted R² = .698, F (5, 300) = 142.02, p < .001. These findings imply that enterprises should integrate predictive strength with calibrated probabilities, transparent explanations, and clear risk communication to improve reliable analytics-supported decisions.