XGBoost
Research on Monitoring and Identification of Stakeholder Financial Risks Based on Multi-Source Heterogeneous Data
Abstract: Stakeholder financial risk is one of the most socially harmful risks in financial risks. Effectively identifying stakeholder financial risks and safeguarding financial security is the core of financial risk prevention. In the field of stakeholder financial risk prevention, information extraction from multi-source heterogeneous data and the combination of data models are crucial. This article … Read more
Feature Importance Analysis and Selection with XGBoost in Python
The benefit of using ensemble decision tree methods like gradient boosting is that they can automatically provide estimates of feature importance from trained predictive models. In this article, you will discover how to estimate the importance of features for predictive modeling problems using the XGBoost library in Python. After reading this article, you will know: … Read more
Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Based on Finite Element Simulation and XGBoost Algorithm
Reference Paper Jianping Lin, Chengwei Qi, Hailang Wan, et al. Prediction of Cross-Tension Strength of Self-Piercing Riveted Joints Using Finite Element Simulation and XGBoost Algorithm. Chinese Journal of Mechanical Engineering, 2021 34: 36. Research Background and Purpose The use of high-strength aluminum alloys is an important means of lightweighting in automobiles. However, due to the … Read more
Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model
Click the blue text to follow us 2023, Issue 2 Nutritional Component Analysis and Hypertension Prediction Based on XGBoost Model Jiang Huai, Tan Lang, Li Shijie, Liu Yu, Wang Junfeng Abstract: Hypertension is a common chronic disease, and early detection and intervention can reduce the risk of complications. Although the onset and development of hypertension … Read more
Summary of Hessian Matrix Application in XGBoost
Click on the above“Beginner’s Guide to Vision” to choose to add a Star Mark or “Top” Important content delivered promptly Introduction The most common application of the Hessian matrix is in the Newton method optimization algorithm, which primarily seeks the extrema of a function where the first derivative is zero. This article provides a clear … Read more
Explaining XGBoost Regression Algorithm to a 10-Year-Old
When I first started exploring machine learning algorithms, I was overwhelmed by all the mathematical content. I found that without fully understanding the intuition behind the algorithm, it was difficult to grasp the underlying mathematical principles. Therefore, I tend to favor explanations that break down the algorithm into simpler, more digestible steps. This is what … Read more
Machine Learning 9.4D XG Algorithm 4: Second Order Approximation
XGBoost utilizes a second technique which is second-order optimization, expanding the loss function l(x,y) using a Taylor series expansion. To approximate it to the second order. This is relatively unique in the XGBoost algorithm, differing from the approach of optimizing using gradient descent in GBT, and also different from Adaboost which increases the weights of … Read more
Short-Term Wind Power Prediction Model Based on Transform Domain Analysis and XGBoost Algorithm
This Issue Selection 2024 Issue 9 Short-Term Wind Power Prediction Model Based on Transform Domain Analysis and XGBoost Algorithm Wang Yongsheng, Li Hailong, Guan Shijie, Wen Caifeng, Xu Zhiwei, Gao Jing DOI: 10.13336/j.1003-6520.hve.20231942 Read Full Text 01Research Background Research Background Under the multiple constraints of energy resources, ecosystems, and socio-economics, global energy security is undergoing … Read more
Building XGBoost Classification Model with Tidymodels
Introduction Reference code homepage, still the great Julia Silge’s code, who is also a main author of tidymodels. Overall process The original official tutorial URL: https://juliasilge.com/blog/xgboost-tune-volleyball/ Notes 1. Due to poor external data currently, the data used is the test data from the tidytuesdayR package. 2. Tidymodels is an integrated R language machine learning environment … Read more