OPTIMIZATION OF MODEL INTEGRATION AND QUANTITATIVE SCORE MAPPING FOR COMPLEX DECISION - MAKING ENVIRONMENTS

In highly complex and dynamically changing decision-making environments, constructing predictive models with strong generalization capabilities, robustness, and high interpretability based on large-scale heterogeneous data has become an important research topic in the field of intelligent modeling. Targeting the deficiencies of traditional models in modeling nonlinear relationships, capturing high-dimensional feature interactions, and outputting consistent results, this paper proposes an end-to-end advanced predictive modeling framework. This framework integrates hierarchical model stacking ensemble and adaptive hyperparameter optimization techniques, enhancing predictive accuracy through knowledge collaboration among models and effectively suppressing overfitting risks. In model result evaluation, multiple metrics such as ROC-AUC, KS index, Precision, Recall, and F1-Score are comprehensively introduced to ensure the robust performance of the model under complex and uncertain conditions. Meanwhile, through Permutation Importance, Partial Dependence Plot (PDP), and the SHAP interpretability framework, transparent explanations at both the global and local levels of the model are realized, effectively revealing the nonlinear driving effects and interaction mechanisms of high-impact features. To address the consistency and comparability of predictive results in cross-scenario decision-making, this paper further constructs a standardized score mapping mechanism based on log-odds transformation, mapping model outputs to a continuous and interpretable score range, enhancing the intuitive interpretability and system adaptability of model results. Comparative experimental results verify the comprehensive advantages of the proposed framework in terms of predictive accuracy, interpretability, and output standardization, providing a complete and scalable technical paradigm for intelligent decision-making in complex systems.