XGBoost | BestHub

Bilibili Tech

Feb 18, 2025 · Artificial Intelligence

Algorithmic Empowerment of Bilibili Streaming: VOD Transcoding Decision, Resource Estimation, and Live Comment Semantic Analysis

The article details how Bilibili leverages AI algorithms—including XGBoost, statistical rules, XDeepFM, and fine‑tuned SBERT—to optimize VOD transcoding decisions, estimate compute resources and processing time, and analyze live comments, thereby boosting streaming efficiency, utilization, and user experience.

AIResource EstimationStreaming Analytics

0 likes · 19 min read

Algorithmic Empowerment of Bilibili Streaming: VOD Transcoding Decision, Resource Estimation, and Live Comment Semantic Analysis

Python Programming Learning Circle

Apr 26, 2024 · Artificial Intelligence

Five Essential Python Libraries for Machine Learning Engineers

This article introduces five essential Python libraries—MLflow, Streamlit, FastAPI, XGBoost, and ELI5—that every junior or intermediate machine‑learning engineer and data scientist should master to streamline experiment tracking, build interactive web apps, deploy models efficiently, achieve fast accurate predictions, and improve model interpretability.

ELI5MLflowStreamlit

0 likes · 8 min read

Five Essential Python Libraries for Machine Learning Engineers

Didi Tech

Jan 25, 2024 · Artificial Intelligence

Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges

Didi’s new Ray‑native XGBoost training platform replaces the fault‑prone Spark solution with a fully Pythonic, fault‑tolerant architecture that leverages Ray’s autoscaling and gang‑scheduling, delivering 2–6× speedups, reduced failure rates, efficient sparse‑vector handling, scalable hyper‑parameter search, and improved resource utilization for large‑scale machine‑learning workloads.

Hyperparameter OptimizationRayXGBoost

0 likes · 20 min read

Ray-native XGBoost Training Platform: Architecture, Performance, and Technical Challenges

360 Quality & Efficiency

Aug 4, 2023 · Artificial Intelligence

Machine Learning Model Testing Workflow and Best Practices

This article outlines the essential concepts, data preparation, model creation, training, deployment, and verification steps for testing machine‑learning models, highlighting dataset requirements, algorithm categories, framework choices, resource considerations, and provides a sample inference request.

AIModel DeploymentModel Testing

0 likes · 7 min read

Machine Learning Model Testing Workflow and Best Practices

NetEase Cloud Music Tech Team

Nov 18, 2022 · Artificial Intelligence

Machine Learning-Based Anomaly Detection for Core Business Metrics

The paper proposes a containerized, machine‑learning framework that fuses rule‑based and XGBoost‑driven anomaly detection to monitor daily active users on a cloud music platform, achieving 89 % recall, 81 % precision and up to 74 % recall improvement over traditional threshold methods, while outlining future model refinement and broader metric applicability.

3-sigmaAnomaly DetectionData Intelligence

0 likes · 11 min read

Machine Learning-Based Anomaly Detection for Core Business Metrics

Model Perspective

Oct 27, 2022 · Artificial Intelligence

Unlocking Black‑Box Models: A Practical Guide to PDP, ICE, and Post‑Hoc Interpretation

This article explains why post‑hoc interpretation methods such as PDP, ALE, LIME, and SHAP are essential for extracting insights from complex machine‑learning models, demonstrates their mathematical foundations, discusses limitations, and provides a complete Python example using XGBoost on a housing‑price dataset.

ICELIMESHAP

0 likes · 14 min read

Unlocking Black‑Box Models: A Practical Guide to PDP, ICE, and Post‑Hoc Interpretation

Model Perspective

Sep 27, 2022 · Artificial Intelligence

Master XGBoost: Boosting Trees Explained with Python Code

This article explains the core concepts of XGBoost as a boosting tree algorithm, describes how it builds ensembles of decision trees to predict outcomes, and provides complete Python implementations for classification and regression using the Scikit-learn interface, along with visualizations of trees and feature importance.

BoostingPythonXGBoost

0 likes · 4 min read

Master XGBoost: Boosting Trees Explained with Python Code

DataFunSummit

Sep 14, 2022 · Artificial Intelligence

Vertical Federated XGBoost (XGB) Algorithm: Problem Definition, Secure Training, Optimization, and Prediction

This article presents a comprehensive overview of the vertical federated XGB algorithm, covering its problem definition, secure multi‑party training techniques, performance‑optimizing oblivious permutation methods, and prediction workflow, while evaluating its scalability and efficiency under various network conditions.

Federated LearningPrivacy-PreservingXGBoost

0 likes · 12 min read

Vertical Federated XGBoost (XGB) Algorithm: Problem Definition, Secure Training, Optimization, and Prediction

NetEase Yanxuan Technology Product Team

May 5, 2022 · Artificial Intelligence

Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan

NetEase Yanxuan built an end‑to‑end time‑series forecasting system for e‑commerce that integrates rich user, product, business and external features with a suite of statistical, machine‑learning and deep‑learning models, delivers predictions via a Tornado‑based service for thousands of SKUs, warehouses, advertising and app traffic, and shows that simpler models like XGBoost often outperform complex deep nets while interpretability and external shocks remain key challenges.

Deep LearningSales PredictionXGBoost

0 likes · 10 min read

Time Series Forecasting Algorithm System in E-commerce: Practice and Applications at NetEase Yanxuan

Sohu Tech Products

Jul 21, 2021 · Artificial Intelligence

Kaggle Jane Street Market Prediction Competition Summary and Model Insights

This article summarizes the author's participation in the Kaggle Jane Street Market Prediction competition, detailing the anonymous feature dataset, utility‑score metric, data preprocessing, the combined AE‑MLP and XGBoost modeling approach, threshold tuning, experimental findings, and references for further study.

KaggleMLPMarket Prediction

0 likes · 8 min read

Kaggle Jane Street Market Prediction Competition Summary and Model Insights

58 Tech

Dec 25, 2020 · Artificial Intelligence

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

This article presents a comprehensive study on C‑side user identity recognition for internet platforms, addressing cold‑start and sample‑scarcity challenges by comparing keyword matching, XGBoost, TextCNN, a fusion model, and an improved Wide & Deep architecture, showing that the latter achieves the highest F1 score of 80.67%.

Cold StartTextCNNXGBoost

0 likes · 13 min read

User Identity Recognition on Internet Platforms: Solving Cold‑Start with Keyword Matching, XGBoost, TextCNN, and an Improved Wide & Deep Model

360 Quality & Efficiency

Dec 20, 2019 · Artificial Intelligence

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

This article describes a complete pipeline for recommending automated test scripts for APK releases, covering CSV data preprocessing, feature encoding, tokenization with pkuseg and jieba, and training various machine‑learning models such as LDA, word2vec, XGBoost, deep neural networks, and multi‑label classifiers to predict script execution order.

APK testingDeep LearningXGBoost

0 likes · 14 min read

Automated APK Test Script Recommendation: Data Processing and Model Training Pipeline

Ctrip Technology

Dec 12, 2019 · Artificial Intelligence

Applying XGBoost for Learning-to-Rank in Ctrip Search: Feature Engineering and Model Practice

This article details how Ctrip's search team leverages XGBoost for learning-to-rank, covering L2R concepts, feature engineering, data preparation, model training, hyper‑parameter tuning, offline evaluation, and deployment insights for large‑scale search ranking systems.

XGBoostfeature engineeringlearning to rank

0 likes · 12 min read

Applying XGBoost for Learning-to-Rank in Ctrip Search: Feature Engineering and Model Practice

58 Tech

Nov 11, 2019 · Artificial Intelligence

Design and Implementation of the 58 Car Price Estimation System Using Machine Learning

The article describes the end‑to‑end architecture, data collection, preprocessing, feature engineering, model selection, training, and hyper‑parameter tuning of 58’s car price estimation platform, which leverages Spark, XGBoost, LightGBM and custom business rules to predict vehicle resale values.

LightGBMXGBoostcar price estimation

0 likes · 11 min read

Design and Implementation of the 58 Car Price Estimation System Using Machine Learning

Didi Tech

Oct 8, 2019 · Artificial Intelligence

Didi and Ant Financial Co‑Develop SQLFlow to Bring AI Capabilities to Data Analysts

Partnering with Ant Financial, Didi enhanced the open-source SQLFlow platform—translating SQL into end-to-end AI workflows with added deep-learning, XGBoost, clustering and SHAP explanation capabilities and Hive support—to create a “SQL garden” marketplace where analysts can deploy ready-made AI models via simple SQL, speeding enterprise AI adoption.

AISHAPXGBoost

0 likes · 9 min read

Didi and Ant Financial Co‑Develop SQLFlow to Bring AI Capabilities to Data Analysts

AntTech

Sep 27, 2019 · Artificial Intelligence

Didi and Ant Financial Co‑Develop SQLFlow to Bring AI Capabilities to Data Analysts

The article describes how Didi's data science team partnered with Ant Financial to co‑build the open‑source SQLFlow platform, enabling analysts to launch AI models via simple SQL, detailing the models contributed, technical extensions, and the broader vision for a universal AI ecosystem.

AISHAPXGBoost

0 likes · 8 min read

DataFunTalk

Jun 6, 2019 · Artificial Intelligence

Design and Machine Learning Practices for Automotive Finance Risk Control

This article outlines the end‑to‑end design of automotive finance risk‑control processes, discusses key data integrity and customer segmentation considerations, and details machine‑learning modeling practices—including logistic regression, decision trees, GBDT, XGBoost, LightGBM and CatBoost—along with an automated platform to streamline model development and deployment.

Automotive FinanceData IntegrityGBDT

0 likes · 17 min read

Design and Machine Learning Practices for Automotive Finance Risk Control

37 Interactive Technology Team

Apr 28, 2019 · Artificial Intelligence

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

By applying XGBoost‑based regression models that are retrained daily on two‑week order data and tuned per sub‑package, the mobile‑game recharge monitoring system predicts 10‑minute order volumes, sharply cuts false alarms from hundreds to dozens, and delivers precise, scalable alerts for game operations.

Anomaly DetectionXGBoostmachine learning

0 likes · 8 min read

Application of Machine Learning Algorithms in Mobile Game Recharge Monitoring

Tencent Advertising Technology

Apr 23, 2019 · Artificial Intelligence

Tencent Advertising Algorithm Competition: Experience and Tips from the Runner‑Up

This article shares the experience of Xu An, runner‑up in the 2019 Tencent Advertising Algorithm Competition, detailing practical advice on feature engineering, model selection, efficiency tricks, personal habits, contest rhythm, and learning resources for aspiring participants.

Algorithm ContestLightGBMTencent Advertising Competition

0 likes · 6 min read

Tencent Advertising Algorithm Competition: Experience and Tips from the Runner‑Up

58 Tech

Sep 7, 2018 · Artificial Intelligence

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com

The article details how 58.com’s Cupid push control system leverages machine‑learning models, especially XGBoost‑based CTR prediction, to prioritize and filter billions of daily push notifications, improving click‑through rates, reducing user annoyance, and providing a scalable, data‑driven architecture for diverse business services.

AB testingCTR predictionSystem Architecture

0 likes · 13 min read

Cupid Push Control System: Machine‑Learning‑Driven Notification Optimization at 58.com