mlsanityData to 14 Oct 2026

A Comparative Analysis of XGBoost

arXiv 1911.01914 ↗First posted 5 Nov 2019cs.LG stat.MLcited by 80

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Abstract (arXiv)

XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver. This work proposes a practical analysis of how this novel technique works in terms of training speed, generalization performance and parameter setup. In addition, a comprehensive comparison between XGBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using the default settings. The results of this comparison may indicate that XGBoost is not necessarily the best choice under all circumstances. Finally an extensive analysis of XGBoost parametrization tuning process is carried out.

Key facts. Cited by 80 papers in the map (data through 14 Oct 2026). First posted on arXiv 5 Nov 2019.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 4; 2022: 9; 2023: 11; 2024: 24; 2025: 26; 2026: 6.

80 papers.

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  1. 2021Boost then Convolve: Gradient Boosting Meets Graph Neural Networkscited by 44
  2. 2021Zero Time Waste: Recycling Predictions in Early Exit Neural Networkscited by 39
  3. 2023A review of ensemble learning and data augmentation models for class imbalanced problems: combination, implementation and evaluationcited by 25
  4. 2025Transformer Copilot: Learning from The Mistake Log in LLM Fine-tuningcited by 21
  5. 2024A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasetscited by 15
  6. 2021Scalable Gaussian Processes for Data-Driven Design using Big Data with Categorical Factorscited by 11
  7. 2024Benchmark Data Repositories for Better Benchmarkingcited by 10
  8. 2022A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learningcited by 8
  9. 2023HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shiftscited by 8
  10. 2023Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBMcited by 5
  11. 2023Benchmarking state-of-the-art gradient boosting algorithms for classificationcited by 4
  12. 2025MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphingcited by 4
38 more
  1. 2022The MIT Supercloud Workload Classification Challengecited by 3
  2. 2023Overview of AdaBoost : Reconciling its views to better understand its dynamicscited by 3
  3. 2024Leveraging graph neural networks for supporting Automatic Triage of Patientscited by 3
  4. 2022Condensed Gradient Boostingcited by 2
  5. 2023A Gradient Boosting Approach for Training Convolutional and Deep Neural Networkscited by 2
  6. 2023Embeddings for Tabular Data: A Surveycited by 2
  7. 2023Interpretable Ensembles of Hyper-Rectangles as Base Modelscited by 2
  8. 2024Optimizing BioTac Simulation for Realistic Tactile Perceptioncited by 2
  9. 2025TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimizationcited by 2
  10. 2025Optical Ocean Recipes: Creating Realistic Datasets to Facilitate Underwater Vision Researchcited by 2
  11. 2026ITBoost: Information-Theoretic Trust for Robust Boostingcited by 2
  12. 2026Causal multi-modal AI for personalized chemosensitivity predictioncited by 2
  13. 2022Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important featurescited by 1
  14. 2022Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANAcited by 1
  15. 2022Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case studycited by 1
  16. 2022A Data-driven Case-based Reasoning in Bankruptcy Predictioncited by 1
  17. 2022OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Datacited by 1
  18. 2022GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationshipscited by 1
  19. 2024Predicting Fairness of ML Software Configurationscited by 1
  20. 2024Histogram-Based Federated XGBoost using Minimal Variance Sampling for Federated Tabular Datacited by 1
  21. 2024Exploring Speech Pattern Disorders in Autism using Machine Learningcited by 1
  22. 2024Interpretable classification of wiki-review streamscited by 1
  23. 2024Integrating White and Black Box Techniques for Interpretable Machine Learningcited by 1
  24. 2024Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projectscited by 1
  25. 2024Supervised Score-Based Modeling by Gradient Boostingcited by 1
  26. 2024Classifying Simulated Gait Impairments using Privacy-preserving Explainable Artificial Intelligence and Mobile Phone Videoscited by 1
  27. 2024Semi-decentralized Training of Spatio-Temporal Graph Neural Networks for Traffic Predictioncited by 1
  28. 2025Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learningcited by 1
  29. 2025Robust-Multi-Task Gradient Boostingcited by 1
  30. 2025Symbolic Quantile Regression for the Interpretable Prediction of Conditional Quantilescited by 1
  31. 2026Robust multi-task boosting using clustering and local ensemblingcited by 1
  32. 2021Machine-learning-based investigation on classifying binary and multiclass behavior outcomes of children with PIMD/SMIDcited by 0
  33. 2023Digital elevation model correction in urban areas using extreme gradient boosting, land cover and terrain parameterscited by 0
  34. 2023A Boosted Machine Learning Framework for the Improvement of Phase and Crystal Structure Prediction of High Entropy Alloys Using Thermodynamic and Configurational Parameterscited by 0
  35. 2023Smart OMVI: Obfuscated Malware Variant Identification using a novel datasetcited by 0
  36. 2024Evaluating Explanatory Capabilities of Machine Learning Models in Medical Diagnostics: A Human-in-the-Loop Approachcited by 0
  37. 2024Advanced Detection of Source Code Clones via an Ensemble of Unsupervised Similarity Measurescited by 0
  38. 2024Classification Modeling with RNN-Based, Random Forest, and XGBoost for Imbalanced Data: A Case of Early Crash Detection in ASEAN-5 Stock Marketscited by 0

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