A Multi-Strategy Learning Framework for Multi-Factor Forecasting and Optimization: Integrating Gradient Boosting, Causality Analysis, and SVM

This paper investigates a data-driven forecasting and decision optimization problem under multi-factor constraints, aiming to predict target outcomes and support resource allocation strategies. We first propose a hybrid computational framework combining multivariate regression and gradient boosted decision trees to handle incomplete data and capture nonlinear dependencies. A secondary modeling scheme integrates random forest classifiers and support vector machines to quantify indirect influence factors and prioritize strategic investments. Extensive experiments demonstrate that our model achieves a prediction error margin within 35% on most instances, while offering interpretable insights via SHAP-based attribution. The findings suggest the framework's robustness and adaptability across diverse scenarios, providing a generalizable solution for simulation-based outcome prediction and strategic decision-making in resource-limited environments.