Understanding XGBoost: A Comprehensive Guide

Understanding XGBoost: A Comprehensive Guide

SIGAI Recommendation SIGAI Resource Summary 60% off on courses taught by Teacher Lei Ming XGBoost is a hot algorithm suitable for analyzing abstract data problems, achieving great results in competitions like Kaggle. Although there are many articles introducing the principles and use of XGBoost, few can clearly and thoroughly explain its principles. The goal of … Read more

An Explanation and Derivation of the XGBoost Algorithm

An Explanation and Derivation of the XGBoost Algorithm

This article is excerpted from “Introduction to Machine Learning Basics (Micro Course Version)” 10.5 XGBoost Algorithm XGBoost is a machine learning algorithm based on the gradient boosting algorithm (GBDT) invented by PhD student Tianqi Chen from the University of Washington in February 2014. This algorithm not only has excellent learning performance but also trains efficiently, … Read more

How to Use XGBoost for Time Series Forecasting

How to Use XGBoost for Time Series Forecasting

↑↑↑ Follow and Star “Datawhale” Daily insights & monthly study groups, don’t miss out Datawhale Insights Source: Jason Brownlee, Organized by Data Science THU This article is approximately 3300 words, and is recommended to read in 10minutes This article introduces how to use XGBoost for time series forecasting, including transforming time series into a supervised … Read more

Basics of Machine Learning: Machine Learning and Materials/Chemistry

Basics of Machine Learning: Machine Learning and Materials/Chemistry

How to Obtain 1. Follow the public account below, and click 【Like】 and 【View】 in this article 2. Click 【Get Course】 in the public account to obtain this material There is a course on Basics of Machine Learning: Machine Learning and Materials/Chemistry Basics of Machine Learning: Machine Learning and Materials/Chemistry 1. Introduction to Machine Learning … Read more

Predicting POD24 in Follicular Lymphoma Using ML Models

Predicting POD24 in Follicular Lymphoma Using ML Models

POD24 significantly affects the overall survival (OS) of patients with follicular lymphoma (FL) within 24 months of disease progression. Existing FL risk scoring models have been developed using statistical methods such as logistic regression or Cox regression. The rapid development of machine learning has facilitated the advancement of prognostic models. In China, Professor Song Yuqin, … Read more