Source: Basics and Advanced of Deep Learning This article is approximately 2800 words long and is suggested to be read in 9 minutes. This article visually demonstrates the prediction process of the XGBoost machine learning model to help you better understand it. The algorithm using XGBoost often achieves good results in Kaggle and other data … Read more
XGBoost (eXtreme Gradient Boosting) has become quite popular in various competitions in recent years due to its excellent predictive performance. Below, we will introduce its principles. Principle First, we need to understand that XGBoost is an improvement of the GBDT algorithm. During the k-th iteration, the loss function of GBDT can be denoted as L(y,F[k](x)). … Read more
Introduction Since the beginning of the 21st century, with the rapid development of the social economy, the demand for resources has continued to increase. However, shallow mineral resources are increasingly depleted, forcing mining work to shift underground. After blasting and excavating deep tunnels, the surrounding rock inevitably produces a loose circle due to the coupling … Read more
Why are tree-based machine learning methods, such as XGBoost and random forests, superior to 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 with deep models, summarizing three reasons to explain this phenomenon. Deep learning has made significant progress … Read more
This article will explain in detail the introduction, functionality, and value ranges of the ten most commonly used hyperparameters in XGBoost, and how to use Optuna for hyperparameter tuning. For XGBoost, the default hyperparameters work fine, but if you want to achieve the best performance, you need to adjust some hyperparameters to match your data. … Read more
Boosting algorithms are a class of machine learning algorithms that build a strong classifier by iteratively training a series of weak classifiers (usually decision trees). In each round of iteration, the new classifier is designed to correct the errors of the previous classifier, thus gradually improving the overall classification performance. Despite the rise and popularity … Read more
Author: Artem Shelamanov Translator: Chen Zhiyan Proofreader: Zhao Ruxuan This article is about 2800 words long, recommended reading time is 5 minutes. This article introduces machine learning libraries, and once you master the model architectures, you can train models to solve real-world problems. If you are a junior or mid-level machine learning engineer or data … Read more
Acute Kidney Injury (AKI) is a common complication in critically ill patients with sepsis, often associated with poor prognosis. This study aims to construct and validate an interpretable prognostic prediction model for sepsis-associated AKI (S-AKI) patients using machine learning (ML) methods. The training cohort data was sourced from the MIMIC-IV database, and the external validation … Read more
Click the blue WeChat name under the title to quickly follow This article is published in: Chinese Journal of Internal Medicine, 2022, 61(2): 185-192. Authors: Wang Kai, Yang Junjie, Liu Ziwan, Dou Guanhua, Wang Xi, Shan Dongkai, Chen Yundai Abstract Objective To develop a pretest probability model for obstructive coronary artery disease in the Chinese … Read more
1. Author Information Authors: Shao Bilin, Ren Meng, Tian Ning Affiliation: School of Management, Xi’an University of Architecture and Technology, Xi’an, Shaanxi 710311 Author Biography: Shao Bilin (1965-), male, professor, master’s supervisor, doctoral supervisor, research interests include big data, artificial intelligence, data information and management, energy sustainable development. E-mail: [email protected] Corresponding Author: Ren Meng (1999-), … Read more