Advancing Predictive Models: Unveiling LightGBM Machine Learning for Data Analysis

Machine learning based models provide substantial improvements in capturing more complex relationships and trends. In this study, we employed a widely used machine learning algorithm called LightGBM. LightGBM is a decision tree based gradient enhancement framework that excels in nonlinear modeling. Compared to traditional methods, LightGBM can autonomously recognize features and discover deeper patterns and associations from massive data, ensuring high prediction accuracy and fast training time. In addition, it also has the ability to efficiently process high-dimensional data and large-scale datasets, ensuring prediction accuracy and training efficiency. By using the LightGBM algorithm, we aim to improve the accuracy, reliability, and interpretability of predictions, providing investors with more comprehensive and accurate information.