This content is sourced from “Surveying and Mapping Bulletin” 2023, Issue 6, Review No.: GS Jing (2023) 1164. Modeling Surface Reflectance in Near Ultraviolet Using XGBoost Aoyong1, Li Hongli2,3, Zhang Wenjuan3, Qin Meng2,3 1. College of Land Engineering, Chang’an University, Xi’an, Shaanxi 710054;2. College of Earth Sciences and Resources, Chang’an University, Xi’an, Shaanxi 710054;3. Aerospace … Read more
Organized by Machine Heart Author: Jiang Siyuan Recently, ApacheCN has opened the XGBoost Chinese documentation project, which provides installation steps, usage tutorials, and tuning tips related to XGBoost. The project has currently completed 90% of the original English documentation, and Machine Heart briefly introduces this documentation and hopes that readers can help improve it. Chinese … Read more
Top Quantitative Self-Media in the Industry Author: Smith Translated by: Fang’s Mantou 1 Introduction Using machine learning to predict the next period’s price or direction based on stock prices is not new, and it does not yield any meaningful predictions. In this article, we will break down the time series data of a series of … Read more
XGBoost: The Winning Tool in Machine Learning! Hello everyone, I’m Mao Ge! Today, I want to introduce you to a magical tool that often wins championships in machine learning competitions – XGBoost. As a gradient boosting framework, XGBoost has become one of the favorite tools among data scientists due to its powerful performance and efficient … Read more
Background The XGBoost model, known as a powerful “weapon” in machine learning, is widely used in data science competitions and industrial applications. The official XGBoost also provides runnable code for various platforms and environments, such as XGBoost on Spark for distributed training. However, in the official implementation of XGBoost on Spark, there exists an instability … Read more
There are many algorithms in machine learning, but today we will focus on one called XGBoost. This tool is quite a big deal in the data science community, with many experts saying it is fast, accurate, and hassle-free. But don’t be afraid, it may sound fancy, but it’s basically a tool that helps us find … Read more
Machine Learning Author: louwill Machine Learning Lab Although deep learning is currently very popular, Boosting algorithms represented by XGBoost, LightGBM, and CatBoost still have a wide range of applications. Setting aside deep learning applications suitable for unstructured data such as images, text, speech, and video, Boosting algorithms are still the first choice for structured data … Read more
Datawhale Insights Source: Coggle Data Science During the process of reading and training on large-scale datasets, iterative data loading is a very suitable choice, and Pytorch supports this method of iterative reading. Next, we will introduce the iterative loading method of XGBoost. Memory Data Loading class IterLoadForDMatrix(xgb.core.DataIter): def __init__(self, df=None, features=None, target=None, batch_size=256*1024): self.features = … Read more
Click the “Datawhalee” above and select “star” in the official account Get valuable content instantly This is the third article on decision trees, mainly introducing mainstream ensemble algorithms based on the Boosting framework, including XGBoost and LightGBM. Here is the complete mind map: XGBoost XGBoost is a tool for large-scale parallel boosting trees. It is … Read more
Author: Tianqi Chen, graduated from Shanghai Jiao Tong University ACM class, currently studying at the University of Washington, engaged in large-scale machine learning research. Note: truth4sex 1. Introduction At the invitation of @Longxing Biaoju, I am writing this article. As a very effective machine learning method, Boosted Trees are one of the most commonly used … Read more