Advancing Predictive Models: Unveiling LightGBM Machine Learning for Data Analysis
Abstract
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.
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LGB is particularly well equipped to address the challenges posed by imbalanced datasets.
“Furthermore, this model is particularly well equipped to address the challenges posed by imbalanced datasets.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [35]
One key advantage of LGB is its ability to handle mixed-type features, which helps preserve the true distribution of these variables within the model.
“One key advantage of LGB is its ability to handle mixed-type features, this helps preserve the true distribution of these variables within the model.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [35]
LGB uses a histogram-based decision tree approach, discretizing continuous features during the data-splitting process, and thereby allowing for effective management of sparse data.
“Additionally, LGB uses a histogram-based decision tree approach, discretizing continuous features during the data-splitting process, and thereby allowing for effective management of sparse data.”
From Machine learning-driven prediction model for successful weaning of patients from mechanical ventilation in ICU (2026) · cites this paper as [35]
Cited by
Sorted by citations
Qiu, C., Tang, L., Zhuang, Y., Chi, C., Zheng, K., & Zhu, X. (2026) · Intensive Care Medicine Experimental · doi:10.1186/s40635-026-00859-8 · Cited by 1 · see citing works
“Although we took steps to mitigate the imbalance during data preprocessing, employing a model that is inherently proficient in managing such issues is more suitable for our objectives [35–37].”
- Authors
- Cai, Z., Huang, H., Sun, G., Li, Z., & Ouyang, C.
- Affiliations
- Hunan University of Finance and Economics (CN)
- ORCID
- Huang, HuaJun
- Venue
- 2023 4th International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI)
- Publisher
- IEEE
- Published
- 2023-12-15
- Pages
- 109-112
- OpenAlex
- W4399530316
- Cited by
- 11
- References
- 0
- Citations by year
- 2026: 7 · 2025: 4
- FWCI
- 6.04
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computer, Big Data and Artificial Intelligence (ICCBD+AI) (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
- Physical Sciences › Computer Science › Information Systems › Data Mining and Machine Learning Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and ELM
- Keywords
- LightGBM, gradient boosting decision tree, predictive modeling, high-dimensional data, large-scale datasets, nonlinear models, feature selection, prediction accuracy, interpretability, investment decision support
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