– 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
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
XGBoost: A Super Useful Python Library! XGBoost is quite renowned in the machine learning community! It’s particularly useful for data mining and predictions. Why? Because it’s accurate! And it’s fast! Today, I’ll chat with you about XGBoost, ensuring you understand it right away! What is XGBoost? XGBoost, short for Extreme Gradient Boosting, sounds quite mysterious, … 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
Hello everyone, today let’s talk about XGBoost~ XGBoost (Extreme Gradient Boosting) is particularly suitable for variants of Gradient Boosting Decision Trees. It was proposed by Tianqi Chen in 2016 and has been widely popular in machine learning competitions such as Kaggle. The historical background of XGBoost can be traced back to the Gradient Boosting algorithm, … Read more
XGBoost: The Python Tool for Gradient Boosting! Hello everyone! Today I want to share with you a super powerful tool in the field of machine learning—XGBoost. I still remember when I first participated in a Kaggle competition, I noticed that almost all winning solutions used XGBoost, which sparked my strong interest in it. XGBoost is … Read more
When companies interview candidates, the interviewers usually include HR, team leaders, and department managers. The process can be divided into two forms. The first form is where the three interviewers interview the candidates separately. The questions may be the same or different, and each interviewer will give a conclusion based on the candidate’s performance. Finally, … Read more
Core Points:From Principles to Cases, A Complete Summary of XGBoost! Hello, I am Cos Dazhuang~ Recently, many people have been messaging about XGBoost, feeling it’s very useful but still somewhat unclear. Today, we will clarify it from principles, derivation of formulas, to a practical case at the end, hoping it helps! In simple terms, XGBoost … Read more
Programmers transitioning to AI are following this account👇👇👇 1 Data Exploration and Preprocessing 1.1 Competition Review Background As the world’s largest clean energy source, solar energy is renewable and non-polluting compared to coal and oil; as long as there is sunlight, there is solar energy. Therefore, the utilization of solar energy has been prioritized as … Read more