LightGBM | BestHub

Model Perspective

Oct 1, 2022 · Artificial Intelligence

Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python

This article introduces LightGBM, a high‑performance gradient boosting framework, explains its advantages over XGBoost, and provides step‑by‑step Python code for building classification and regression models on the Iris dataset, including model training, evaluation, and visualizing feature importance and tree structures.

Gradient BoostingLightGBMPython

0 likes · 5 min read

Boost Your Models with LightGBM: Fast, Accurate Gradient Boosting in Python

Bitu Technology

Jul 8, 2022 · Artificial Intelligence

Applying NLP and Machine Learning to Classify Tubi User Feedback

This article explains how Tubi leverages natural‑language processing, sentence embeddings (USE and BERT), and LightGBM models to automatically categorize large volumes of Net Promoter Score comments and customer‑support tickets, enabling data‑driven product decisions and workflow automation.

EmbeddingsLightGBMNLP

0 likes · 11 min read

Applying NLP and Machine Learning to Classify Tubi User Feedback

HelloTech

Mar 28, 2022 · Artificial Intelligence

Algorithmic Optimization for Information Flow Advertising at Hello Travel

Hello Travel tackles information‑flow advertising challenges by using LightGBM‑based models to predict order conversion, creative performance, and pre‑bid user quality, augmenting sparse data with feature engineering and uplift techniques, while planning future fully automated delivery, richer pre‑screening, and cross‑channel reinforcement‑learning enhancements.

Feature EngineeringLightGBMadvertising

0 likes · 18 min read

Algorithmic Optimization for Information Flow Advertising at Hello Travel

DataFunTalk

Mar 27, 2022 · Artificial Intelligence

Algorithmic Optimization for Information‑Flow Advertising at Hello Travel

This talk explains how Hello Travel tackles challenges in information‑flow advertising by describing the market landscape, their business background, and detailed algorithmic optimization across plan, creative, and pre‑bid dimensions, including data‑driven modeling, feature engineering, LightGBM and uplift models, and outlines future directions.

LightGBMadvertisingalgorithm optimization

0 likes · 16 min read

Python Programming Learning Circle

Mar 9, 2022 · Artificial Intelligence

Top Python Machine Learning Libraries in 2021 and Their Key Features

This article introduces the most important Python machine‑learning libraries of 2021—including TensorFlow, Scikit‑Learn, NumPy, Keras, PyTorch, LightGBM, Eli5, SciPy, Theano and Pandas—explaining their purposes, distinctive characteristics, and why they are essential tools for modern AI development.

KerasLightGBMPyTorch

0 likes · 12 min read

Top Python Machine Learning Libraries in 2021 and Their Key Features

Amap Tech

Jan 15, 2021 · Artificial Intelligence

Solution Overview of the AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis from In‑Vehicle Video Images

To tackle the AMAP‑TECH competition’s dynamic road‑condition classification from scarce, imbalanced vehicle‑video frames, the team combined YOLOv5 object detection, ResNeXt101‑based semantic embeddings, and engineered temporal detection statistics, feeding the fused features into a five‑fold LightGBM model that achieved top weighted‑F1 performance.

Feature EngineeringLightGBMResNeXt

0 likes · 10 min read

Solution Overview of the AMAP-TECH Algorithm Competition: Dynamic Road Condition Analysis from In‑Vehicle Video Images

Qunar Tech Salon

Dec 10, 2020 · Operations

Improving International Hotel After‑Sales Service: Metrics, Optimization Strategies, and Risk Prediction with LightGBM

The article analyzes the after‑sales process of international hotel bookings, defines key metrics such as defect rate and SPO, describes operational improvements, and presents a LightGBM‑based risk‑prediction model to reduce on‑site defects and enhance overall service efficiency.

LightGBMafter-saleshotel industry

0 likes · 14 min read

Improving International Hotel After‑Sales Service: Metrics, Optimization Strategies, and Risk Prediction with LightGBM

Yuewen Technology

Nov 10, 2020 · Artificial Intelligence

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

This article explains how a binary‑classification model combining estimated future behavior and statistical data is used to compute a unified popularity score for web novels, improving both recall and ranking in search and library scenarios while addressing challenges of cold‑start and long‑tail items.

LambdaMARTLightGBMRecommendation systems

0 likes · 9 min read

Modeling Web Novel Popularity with Predictive Ranking and Statistical Fusion

JD Tech Talk

Aug 4, 2020 · Artificial Intelligence

Champion Chasing Boy Team’s Solution for the 2020 Digital China Innovation Contest – Digital Government Track

The Champion Chasing Boy team presents a comprehensive AI‑driven solution for the 2020 Digital China Innovation Contest, detailing the competition background, data sources, feature engineering, LightGBM modeling, trajectory‑matching algorithms, system architecture, visual analytics, and safety monitoring for maritime vessel activity classification.

AILightGBMMaritime Analytics

0 likes · 10 min read

Champion Chasing Boy Team’s Solution for the 2020 Digital China Innovation Contest – Digital Government Track

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.

Feature EngineeringLightGBMXGBoost

0 likes · 11 min read

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

Tencent Advertising Technology

May 8, 2019 · Artificial Intelligence

Experience Sharing of the 2019 Tencent Advertising Algorithm Competition – Week 1 Champion’s Insights

The week‑1 champion of the 2019 Tencent Advertising Algorithm Competition shares practical experience on data cleaning, feature engineering, model selection (including LightGBM and deep learning), validation strategies, and tips for handling massive ad exposure logs to achieve high SMAPE and monotonicity scores.

LightGBMadvertisingalgorithm competition

0 likes · 6 min read

Experience Sharing of the 2019 Tencent Advertising Algorithm Competition – Week 1 Champion’s Insights

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

JD Retail Technology

Jun 13, 2018 · Artificial Intelligence

IJCAI 2018 International Advertising Algorithm Competition Champion Uses Transfer Learning and LightGBM for Ad Conversion Prediction

The IJCAI 2018 International Advertising Algorithm Competition was won by JD.com algorithm engineer Hua Zhixiang, who employed a two‑stage LightGBM model with transfer learning and carefully designed statistical, temporal, ranking, and representation features to achieve top conversion‑rate predictions on massive e‑commerce advertising data.

Feature EngineeringIJCAILightGBM

0 likes · 5 min read

IJCAI 2018 International Advertising Algorithm Competition Champion Uses Transfer Learning and LightGBM for Ad Conversion Prediction

Tencent Advertising Technology

May 7, 2018 · Artificial Intelligence

Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches

The author, a graduate student and weekly champion of the Tencent advertising algorithm contest, shares practical guidance on selecting mainstream CTR models—including LightGBM, field‑aware factorization machines, and deep learning approaches—while offering tips on feature handling, hyper‑parameter settings, and resource‑efficient implementation.

Deep LearningFFMFeature Engineering

0 likes · 5 min read

Choosing Mainstream CTR Models: LightGBM, FFM, and Deep Learning Approaches