Author: Huang Tianyuan, currently a PhD student at Fudan University, with research involving text mining, social network analysis, and machine learning. I hope to share learning experiences and promote the application of R language in the industry. Email: [email protected] XGBoost is currently the best predictive solution based on tree models, and is worth exploring and … Read more
Original | ETF Trading Strategy Combining Price Momentum and Crowding Source | China Galaxy Securities Research Institute, Typesetting: Python Data Science 1. Introduction This article mainly introduces the trading strategy for margin-financed ETFs. As an index tracking tool, ETFs have good asset allocation value. At the same time, by combining price diffusion and Minsky moments, … Read more
Selected from Ancestry Author: Tyler Folkman Translated by Machine Heart Contributors: Liu Xiaokun, Li Zenan The algorithm using XGBoost often achieves good results in Kaggle and other data science competitions, making it popular (see: Why Does XGBoost Perform So Well in Machine Learning Competitions?). This article analyzes the prediction process of the XGBoost machine learning … Read more
Author: louwill Although deep learning is currently very popular, Boosting algorithms represented by XGBoost, LightGBM, and CatBoost still have a wide range of applications. Leaving aside the applications of deep learning suitable for unstructured data such as images, text, speech, and video, Boosting algorithms remain the first choice for structured data with fewer training samples. … Read more
Author IntroductionIntroduction Su Gaosheng, graduated with a master’s degree in statistics from Southwestern University of Finance and Economics, currently working at China Telecom, mainly responsible for big data analysis and data modeling for existing enterprise customers. Research direction: Machine Learning, favorite programming language: R, no exceptions. E-mail: [email protected] Zero, Case Background Introduction and Modeling Idea … Read more
XGBoost: A Powerful Python Library for Extreme Gradient Boosting Li: Wang, I often feel that my efficiency in data processing and predictive modeling is not high. Is there a good Python library that can help me? 😟 Wang: Of course! 🙌 Today, I will introduce you to XGBoost, which is a great assistant in the … Read more
Core Points:Complete Summary of XGBoost Core Issues! Hello, I am Cos Dazhuang! Today I will share content about XGBoost~ XGBoost is very important, especially excelling in classification, regression, and ranking problems. Its practical applications include financial risk control, medical diagnosis, industrial manufacturing, and advertising click-through rate prediction. With its efficient performance and robustness, XGBoost has … Read more
↑↑↑ Follow “Star Mark” Datawhale Daily Insights & Monthly Study Groups, don’t miss out Datawhale Insights Author: Wang Maolin, Huazhong University of Science and Technology, Datawhale Member Content Overview XGBoost Model and Parameter Tuning Summary XGBoost Principles XGBoost Advantages Summary XGBoost Parameter Details XGBoost Quick Usage XGBoost Parameter Tuning Methods PPT Download: Reply “210502” in … Read more
– What is the basis for tree node splitting in XGBoost? – How is the weight of tree nodes calculated? – What improvements has XGBoost made to prevent overfitting? Those reading this article are likely familiar with XGBoost. Indeed, XGBoost is not only a powerful tool in major data science competitions but is also widely … Read more
Datawhale Insights Source: Machine Heart Editorial Team Why do tree-based machine learning methods, such as XGBoost and Random Forest, outperform deep learning on tabular data?This article provides reasons behind this phenomenon, selecting 45 open datasets and defining a new benchmark to compare tree-based models and deep models, summarizing three key points to explainthis phenomenon. Deep … Read more