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
Compiled by | Yan Zheng Data & AI entrepreneur, investor Oliver Molander recently joked in a LinkedIn post: “If you had asked AI experts what LLMs were before the launch of ChatGPT in 2022, many might have answered that it’s a law degree.” He added that many people find it hard to accept that AI … 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
On Kaggle, 90% of fields including finance, tree models (like XGBoost) outperform deep learning neural network models. Let’s analyze the reasons. 01 Tree VS NN Deep learning neural network models excel in fields such as image processing and natural language, but in tabular data, such as OHLC candlestick data, neither neural networks nor transformers outperform … 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
XGBoost has shone in Kaggle competitions. In previous articles, the principles of the XGBoost algorithm and the XGBoost splitting algorithm were introduced. Most explanations of XGBoost parameters online only scratch the surface, making it extremely unfriendly for those new to machine learning algorithms. This article will explain some important parameters while referring to mathematical formulas … Read more