GBDT | BestHub

DataFunSummit

Aug 27, 2023 · Artificial Intelligence

Privacy-Preserving Gradient Boosting Decision Trees via Multi-Party Computation and the Squirrel Framework

This article introduces a privacy-preserving gradient boosting decision tree (GBDT) solution built on multi‑party computation, detailing its background, training steps, the MPC tools used, and the Squirrel framework’s workflow, while discussing performance challenges and experimental results demonstrating scalability to millions of samples.

GBDTMPCSecure Computation

0 likes · 9 min read

Privacy-Preserving Gradient Boosting Decision Trees via Multi-Party Computation and the Squirrel Framework

GuanYuan Data Tech Team

Jul 14, 2022 · Big Data

How to Train Massive GBDT Models on Spark: A Complete Step‑by‑Step Guide

This article walks through using Apache Spark for large‑scale GBDT training, covering the challenges of massive data, Spark deployment, PySpark code examples, differences from Pandas, feature engineering, mmlspark installation, early‑stopping tricks, performance bottlenecks, and a systematic evaluation of alternative frameworks.

GBDTSparkbig data

0 likes · 38 min read

How to Train Massive GBDT Models on Spark: A Complete Step‑by‑Step Guide

GuanYuan Data Tech Team

Apr 14, 2022 · Artificial Intelligence

Mastering Time Series Forecasting: From Moving Averages to Transformers

Time series forecasting, essential across weather, finance, and commerce, involves tasks like classification, clustering, anomaly detection, and especially prediction; this article explores its definitions, evaluation metrics, traditional methods, machine‑learning approaches, deep‑learning models such as TFT, and emerging AutoML tools, offering practical insights and best practices.

AutoMLDeep LearningGBDT

0 likes · 27 min read

Mastering Time Series Forecasting: From Moving Averages to Transformers

iQIYI Technical Product Team

Jul 23, 2021 · Artificial Intelligence

XGBoost Serving: An Open‑Source High‑Performance Inference System for GBDT and GBDT+FM Models

XGBoost Serving is an open‑source, high‑performance inference system built on TensorFlow Serving that adds dedicated servables for pure GBDT, GBDT+FM binary‑classification, and GBDT+FM multi‑classification models, providing automatic version lifecycle management, GRPC/HTTP APIs, and up to 50 % latency reduction, now available on GitHub after successful deployment in iQIYI’s recommendation platform.

Factorization MachinesGBDTServing Architecture

0 likes · 12 min read

XGBoost Serving: An Open‑Source High‑Performance Inference System for GBDT and GBDT+FM Models

DataFunTalk

Jan 25, 2021 · Artificial Intelligence

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

This article reviews the development of Zhihu's search system, describing the transition from early GBDT ranking to deep neural networks, the introduction of multi‑objective and position‑bias‑aware learning‑to‑rank methods, context‑aware techniques, end‑to‑end training, personalization, and future research directions.

DNNDeep LearningGBDT

0 likes · 17 min read

Evolution of Zhihu Search Ranking Models: From GBDT to DNN, Multi‑Goal and Context‑Aware LTR

Xianyu Technology

Feb 27, 2020 · Artificial Intelligence

Data-Driven Simulation for User Activity Retention Prediction

By extracting hour‑level activity logs and training supervised models—including CART, GBDT, and neural networks—on user tags, the team simulated short‑term metrics for new reward campaigns, enabling earlier prediction of next‑day retention and shortening experiment cycles despite delayed T+1 data.

AB testingCARTGBDT

0 likes · 9 min read

Data-Driven Simulation for User Activity Retention Prediction

360 Quality & Efficiency

Jan 17, 2020 · Artificial Intelligence

File Release Application Prediction Model Using GBDT

This article describes how a GBDT‑based prediction model was built to forecast file release application parameters such as volume ratio, target audience, and gray level, covering data collection, feature engineering, model training, service deployment, and practical considerations for handling bad cases.

GBDTdata preprocessingfile release

0 likes · 8 min read

File Release Application Prediction Model Using GBDT

iQIYI Technical Product Team

Jun 28, 2019 · Artificial Intelligence

iQIYI's RSLIME: A Novel Feature Importance Analysis Method for Video Recommendation Systems

iQIYI introduces RSLIME, a model‑agnostic, sample‑level feature importance method for its three‑stage small‑video recommendation system, enabling interpretable analysis of a complex ranking module that combines DNN, GBDT, and FM, and demonstrating stable, AUC‑correlated insights for optimization and feature selection.

DNNFMGBDT

0 likes · 11 min read

iQIYI's RSLIME: A Novel Feature Importance Analysis Method for Video Recommendation Systems

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

Tencent Advertising Technology

Apr 26, 2019 · Big Data

Handling Large-Scale Data in the Tencent Advertising Algorithm Competition: Model Choices, Data Splitting, and Feature Engineering

The article shares practical strategies for processing massive advertising data in the Tencent algorithm competition, covering model selection between GBDT and neural networks, efficient data partitioning methods for low‑resource environments, and the importance of feature engineering to achieve top rankings.

GBDTNeural NetworksTencent Ads

0 likes · 7 min read

Handling Large-Scale Data in the Tencent Advertising Algorithm Competition: Model Choices, Data Splitting, and Feature Engineering

AntTech

May 22, 2018 · Artificial Intelligence

Unpack Local Model Interpretation for GBDT – Summary and Analysis

This article summarizes the Ant Financial paper presented at DASFAA 2018 that proposes a universal local explanation method for Gradient Boosting Decision Tree models, detailing the problem definition, the PMML‑based algorithm for attributing feature contributions, experimental validation on fraud detection data, and the practical benefits for model transparency and improvement.

GBDTPMMLfeature importance

0 likes · 12 min read

Unpack Local Model Interpretation for GBDT – Summary and Analysis

Tencent Advertising Technology

Apr 29, 2018 · Artificial Intelligence

Insights and Strategies from Winning the Tencent Advertising Algorithm Competition

The author, a Sun Yat‑sen University undergraduate and repeat weekly champion, shares practical tips on handling large datasets, effective feature engineering, and combining GBDT with a custom deepFFM model to achieve top scores in the Tencent advertising algorithm competition.

Data ProcessingGBDTadvertising algorithms

0 likes · 4 min read

Insights and Strategies from Winning the Tencent Advertising Algorithm Competition

Qunar Tech Salon

Apr 3, 2018 · Artificial Intelligence

An Introduction to Gradient Boosting Decision Trees (GBDT) and Its Applications in Consumer Finance

Gradient Boosting Decision Tree (GBDT) is an ensemble learning method that combines additive and gradient boosting, detailed with its mathematical foundations, regression and classification algorithms, implementation using scikit‑learn, and a real‑world consumer‑finance fraud detection case achieving high AUC and KS metrics.

Consumer FinanceGBDTGradient Boosting

0 likes · 11 min read

An Introduction to Gradient Boosting Decision Trees (GBDT) and Its Applications in Consumer Finance

Tongcheng Travel Technology Center

Jan 18, 2018 · Artificial Intelligence

Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation

This article describes a tourism recommendation system that addresses data sparsity, seasonality, and geographic variations by using an offline‑online architecture, GBDT+LR CTR prediction, exponential decay scoring, and extensive feature engineering, achieving a 1.6% conversion‑rate increase and high accuracy and recall.

CTR predictionGBDTfeature engineering

0 likes · 14 min read

Tourism Spot Recommendation System: Framework, Model Construction, Feature Engineering, and Performance Evaluation

iQIYI Technical Product Team

Nov 10, 2017 · Artificial Intelligence

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

The iQIYI recommendation system combines a two‑stage pipeline of recall and ranking, evolving from logistic regression to a GBDT‑FM‑DNN ensemble, using online feature storage, extensive feature engineering, and configurable strategies to deliver personalized video suggestions while addressing feature drift and multi‑objective business goals.

Deep neural networksFactorization MachinesGBDT

0 likes · 13 min read

iQIYI Recommendation System: Architecture, Model Evolution, and Ranking Strategies

Baidu Waimai Technology Team

Jun 27, 2017 · Artificial Intelligence

Detecting Low‑Quality New Users in Food Delivery with a GBDT + LR Model

The article describes a data‑driven approach for identifying low‑value new users in a food‑delivery platform by labeling 7‑day repeat‑purchase behavior, extracting order, behavior, merchant and user features, and training a combined Gradient Boosted Decision Tree and Logistic Regression model to improve fraud detection and merchant penalty decisions.

AIFood DeliveryGBDT

0 likes · 7 min read

Detecting Low‑Quality New Users in Food Delivery with a GBDT + LR Model