Dream Morning Reporting from Aofeisi Quantum Bit | Public Account QbitAI What algorithm is most likely to win prizes in machine learning competitions on Kaggle? You might say: Of course, it’s deep learning. Not really. According to statistics, the most winning algorithms are gradient boosting algorithms like XGBoost. This is strange. Deep learning shines in … 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
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
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
XGBoost is the most famous algorithm for handling different types of tabular data, with LightGBM and Catboost released to address its shortcomings. On September 12, XGBoost released the new version 2.0. This article will not only introduce the complete history of XGBoost but also discuss the new mechanisms and updates. This is a long article, … Read more
The Gradient Boosting Decision Tree (GBDT), as a leader in ensemble learning, has won the favor of many data scientists due to its excellent performance in classification and regression tasks. This article will take you deep into the mysterious veil of GBDT and demonstrate how to efficiently implement GBDT using the sklearn library. 1. What … Read more
A simple technique for integrating decision trees involves training trees on subsamples of the training dataset. A subset of rows from the training data can be used to train individual trees known as bagging. When a subset of rows from the training data is also used when calculating each split point, this is referred to … Read more
Extreme Gradient Boosting (XGBoost) is an open-source library that provides an efficient implementation of the gradient boosting algorithm. Although other open-source implementations of this method existed before XGBoost, the release of XGBoost seems to have unleashed the power of the technique and brought gradient boosting to the attention of the machine learning community at large. … Read more
Click on the “MLNLP” above to select the “Star” public account Heavyweight content delivered to you first This article is the third in a series 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 … Read more
Click on the above “Little White Learns Vision” to choose to add “Star Mark” or “Pin” Important content delivered at the first time XGBoost is one of the most commonly used algorithms with relatively high accuracy in various data mining or machine learning competitions (excluding Deep Learning algorithms). In other words, for those who have … Read more