Optimization of Prediction Model Based on Gradient Boosting Decision Tree

In the context of a data-intensive society, the construction and optimization of predictive models have become increasingly essential. As an integrated learning algorithm, Gradient Boosting Decision Trees (GBDT) offers a robust solution for capturing complex nonlinear relationships and managing high-dimensional feature data. Its applicability spans various industries, including finance, healthcare, and user behavior analysis. Despite the flexibility and accuracy that GBDT models provide, several challenges remain in practical applications, particularly regarding overfitting, training complexity, and escalating computational costs. To address these challenges, researchers have proposed a variety of optimization strategies, including hyperparameter optimization, feature selection, feature engineering, and the development of hybrid models. This paper systematically organizes the methods for optimizing GBDT models, analyzes prevailing optimization strategies, and evaluates their effectiveness across different datasets and application scenarios. Initially, the paper introduces the fundamental principles and application areas of GBDT, followed by a review of relevant literature that highlights its application cases across multiple fields. It subsequently extends the discussion to multidimensional time series analysis, the TFCA integrated learning approach, the AdaBoost algorithm, and the GBDT algorithm. Backtesting experiments are conducted using the CSI 300 constituent stock data to assess the effectiveness of the optimized GBDT model in stock selection and portfolio construction. The findings indicate that the optimized GBDT model significantly enhances prediction accuracy and improves portfolio performance.