Interpretable Machine Learning Framework for Corporate Financialization Prediction: A SHAP-Based Analysis of High-Dimensional Data

Interpretable Machine Learning Framework for Corporate Financialization Prediction: A SHAP-Based Analysis of High-Dimensional Data

High-dimensional prediction problems with complex non-linear feature interactions present significant algorithmic challenges in machine learning, particularly when dealing with imbalanced datasets and multicollinearity issues. This study proposes an innovative Shapley Additive Explanations (SHAP)-en...

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Bibliographic Details
Main Authors: Yanhe Wang, Wei Wei, Zhuodong Liu, Jiahe Liu, Yinzhen Lv, Xiangyu Li
Format: Article
Language:English
Published: MDPI AG 2025-08-01
Series:Mathematics
Subjects:
machine learning
SHAP interpretability
financial prediction modeling
high-dimensional data analysis
corporate social responsibility
U-shaped relationships
Online Access:https://www.mdpi.com/2227-7390/13/15/2526
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