mlsanityData to 14 Oct 2026

CatBoost: unbiased boosting with categorical features

arXiv 1706.09516 ↗First posted 28 Jun 2017cs.LGcited by 31

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Abstract (arXiv)

This paper presents the key algorithmic techniques behind CatBoost, a new gradient boosting toolkit. Their combination leads to CatBoost outperforming other publicly available boosting implementations in terms of quality on a variety of datasets. Two critical algorithmic advances introduced in CatBoost are the implementation of ordered boosting, a permutation-driven alternative to the classic algorithm, and an innovative algorithm for processing categorical features. Both techniques were created to fight a prediction shift caused by a special kind of target leakage present in all currently existing implementations of gradient boosting algorithms. In this paper, we provide a detailed analysis of this problem and demonstrate that proposed algorithms solve it effectively, leading to excellent empirical results.

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). First posted on arXiv 28 Jun 2017.

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Citing papers per year: 2021: 2; 2022: 6; 2023: 2; 2024: 7; 2025: 5; 2026: 9.

31 papers.

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  1. 2025Real-TabPFN: Improving Tabular Foundation Models via Continued Pre-training With Real-World Datacited by 41
  2. 2024An Open Multilingual System for Scoring Readability of Wikipediacited by 7
  3. 2023Stochastic Configuration Machines for Industrial Artificial Intelligencecited by 5
  4. 2024Gradient Boosting Trees and Large Language Models for Tabular Data Few-Shot Learningcited by 5
  5. 2023Fair multilingual vandalism detection system for Wikipediacited by 4
  6. 2025The Impact of Feature Scaling In Machine Learning: Effects on Regression and Classification Taskscited by 4
  7. 2022On the Importance of Image Encoding in Automated Chest X-Ray Report Generationcited by 3
  8. 2025From Decision Trees to Boolean Logic: A Fast and Unified SHAP Algorithmcited by 3
  9. 2026Financial Transaction Retrieval and Contextual Evidence for Knowledge-Grounded Reasoningcited by 2
  10. 2026Quadrature-TreeSHAP: Depth-Independent TreeSHAP and Shapley Interactionscited by 2
  11. 2022DELATOR: Money Laundering Detection via Multi-Task Learning on Large Transaction Graphscited by 1
  12. 2022Machine Learning Methods Applied to Cortico-Cortical Evoked Potentials Aid in Localizing Seizure Onset Zonescited by 1
19 more
  1. 2024Heterogeneous Random Forestcited by 1
  2. 2025Graph-Linguistic Fusion: Using Language Models for Wikidata Vandalism Detectioncited by 1
  3. 2026Tabular foundation models for robust calibration of near-infrared chemical sensing datacited by 1
  4. 2021Generalized Linear Tree Space Nearest Neighborcited by 0
  5. 2021Machine Learning for Real-World Evidence Analysis of COVID-19 Pharmacotherapycited by 0
  6. 2022A multiscale spatiotemporal approach for smallholder irrigation detectioncited by 0
  7. 2022Optimization of Oblivious Decision Tree Ensembles Evaluation for CPUcited by 0
  8. 2022Performance and utility trade-off in interpretable sleep stagingcited by 0
  9. 2024A generalized decision tree ensemble based on the NeuralNetworks architecture: Distributed Gradient Boosting Forest (DGBF)cited by 0
  10. 2024Breast Cancer Classification Using Gradient Boosting Algorithms Focusing on Reducing the False Negative and SHAP for Explainabilitycited by 0
  11. 2024Explainable convolutional neural network model provides an alternative genome-wide association perspective on mutations in SARS-CoV-2cited by 0
  12. 2024Predicting Emergency Department Visits for Patients with Type II Diabetescited by 0
  13. 2025Comparative Analysis of Modern Machine Learning Models for Retail Sales Forecastingcited by 0
  14. 2026WOODELF-HD: Efficient Background SHAP for High-Depth Decision Treescited by 0
  15. 2026Adversarial Creation and Detection of AI-Generated Social Bot Contentcited by 0
  16. 2026Uncertainty quantification in mechanics: A unified Bayesian perspectivecited by 0
  17. 2026Scaling Laws, Tabular Data and Actuarial Ratemaking Modelscited by 0
  18. 2026Which alloy composition,what process parameters? Inferring the recipe from optimized metallic microstructure and texturecited by 0
  19. 2026Estimating Uncoded Crash Factors with Tabular Foundation and System One Models: Kumo Tabular and Jevcited by 0