# A Comparative Analysis of XGBoost

arXiv: https://arxiv.org/abs/1911.01914 · HTML: https://mlsanity.com/paper/1911.01914/

## Key facts

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

## Built on by (most cited first, 30 of 80)

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- [A k-additive Choquet integral-based approach to approximate the SHAP values for local interpretability in machine learning](https://mlsanity.com/paper/2211.02166/) (2022)
- [HyperTime: Hyperparameter Optimization for Combating Temporal Distribution Shifts](https://mlsanity.com/paper/2305.18421/) (2023)
- [Enhanced Gradient Boosting for Zero-Inflated Insurance Claims and Comparative Analysis of CatBoost, XGBoost, and LightGBM](https://mlsanity.com/paper/2307.07771/) (2023)
- [Benchmarking state-of-the-art gradient boosting algorithms for classification](https://mlsanity.com/paper/2305.17094/) (2023)
- [MorphBoost: Self-Organizing Universal Gradient Boosting with Adaptive Tree Morphing](https://mlsanity.com/paper/2511.13234/) (2025)
- [The MIT Supercloud Workload Classification Challenge](https://mlsanity.com/paper/2204.05839/) (2022)
- [Overview of AdaBoost : Reconciling its views to better understand its dynamics](https://mlsanity.com/paper/2310.18323/) (2023)
- [Leveraging graph neural networks for supporting Automatic Triage of Patients](https://mlsanity.com/paper/2403.07038/) (2024)
- [Condensed Gradient Boosting](https://mlsanity.com/paper/2211.14599/) (2022)
- [A Gradient Boosting Approach for Training Convolutional and Deep Neural Networks](https://mlsanity.com/paper/2302.11327/) (2023)
- [Embeddings for Tabular Data: A Survey](https://mlsanity.com/paper/2302.11777/) (2023)
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- [TabGLM: Tabular Graph Language Model for Learning Transferable Representations Through Multi-Modal Consistency Minimization](https://mlsanity.com/paper/2502.18847/) (2025)
- [Optical Ocean Recipes: Creating Realistic Datasets to Facilitate Underwater Vision Research](https://mlsanity.com/paper/2509.20171/) (2025)
- [ITBoost: Information-Theoretic Trust for Robust Boosting](https://mlsanity.com/paper/2605.04671/) (2026)
- [Causal multi-modal AI for personalized chemosensitivity prediction](https://mlsanity.com/paper/2609.13567/) (2026)
- [Churn modeling of life insurance policies via statistical and machine learning methods -- Analysis of important features](https://mlsanity.com/paper/2202.09182/) (2022)
- [Automatic Error Classification and Root Cause Determination while Replaying Recorded Workload Data at SAP HANA](https://mlsanity.com/paper/2205.08029/) (2022)
- [Forecasting COVID-19 spreading trough an ensemble of classical and machine learning models: Spain's case study](https://mlsanity.com/paper/2207.05753/) (2022)
- [A Data-driven Case-based Reasoning in Bankruptcy Prediction](https://mlsanity.com/paper/2211.00921/) (2022)
- [OOG- Optuna Optimized GAN Sampling Technique for Tabular Imbalanced Malware Data](https://mlsanity.com/paper/2212.01274/) (2022)
- [GWRBoost:A geographically weighted gradient boosting method for explainable quantification of spatially-varying relationships](https://mlsanity.com/paper/2212.05814/) (2022)

## 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.