ArticleJournal of big data2020
CatBoost for big data: an interdisciplinary review.
Article in Journal of big data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 334 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
334 citing papers in PubMed.
- Editorial: Prediction of Survival in Hepatocellular Cancer-Rise and Fall of the Machines. Authors' Reply.Alimentary pharmacology & therapeutics · 2026Article
- Machine learning-based screening tool for predicting the risk of oropharyngeal dysphagia in patients with ischemic stroke.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2026Article
- Lysosomal Dysfunction Is Associated With Intervertebral Disc Degeneration: Multiomics and Machine Learning Identify Molecular Subtypes and Hub Genes.The journal of gene medicine · 2026Article
- Machine-learning prediction of urine-culture positivity in a multicentre test-ordered cohort: Model development and internal validation.BJUI compass · 2026Article
- Development and internal validation of an interpretable machine learning prognostic prediction model for postoperative delirium after esophagectomy.Journal of thoracic disease · 2026Article
- Grey Wolf Optimization Inverse Mix Design of Steel Slag Asphalt Mixtures.Materials (Basel, Switzerland) · 2026Article
- Peptide language pragmatic analysis and two-stage hierarchical learning framework for therapeutic peptide prediction.BMC biology · 2026Article
- Predicting lifetime cervical cancer screening among sexually active women in low- and middle-income countries using machine learning models: evidence from Demographic and Health Surveys.AJOG global reports · 2026Article
- On-demand growth of semiconductor heterostructures guided by physics-informed machine learning.Science advances · 2026Article
- PM2.5/PM10 Forecasting System with Benchmarking of 44 Machine Learning Algorithms and Ensemble Learning Approaches.Sensors (Basel, Switzerland) · 2026Article
- Integrated discovery and mechanistic characterization of xanthine oxidase inhibitory peptides from sheep Milk.Food chemistry: X · 2026Article
- Article
- An interpretable machine learning model for diabetic foot risk classification in patients with diabetes.Scientific reports · 2026Article
- Detecting entanglement in high-spin quantum systems via a stacking ensemble of machine learning models.Scientific reports · 2026Article
- SIFA: A two-stage adaptive ensemble framework for solar irradiance forecasting using a wrapper-based feature selection and chaotic manta ray optimization.Scientific reports · 2026Article
- A feature-centric decision-making framework for diagnosing and enhancing system efficiency in intelligent multi-agent manufacturing.Scientific reports · 2026Article
- Quantifying agricultural resilience under climate variability: a data-driven climate resilience index for European cereal systems.Scientific reports · 2026Article
- Development of an Artificial Intelligence Web Application for Predicting Chemotherapy-Induced Neutropenia in Patients With Non-Small Cell Lung Cancer: A Prospective Study.Cancer medicine · 2026Article
- Estimation of hemoglobin concentration at the initiation of cardiopulmonary bypass using support vector regression.The journal of extra-corporeal technology · 2026Article
- Machine learning-based prediction of large-for-gestational-age neonates in diabetic and non-diabetic pregnancies.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
274 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gradient Boosted Decision Trees (GBDT's) are a powerful tool for classification and regression tasks in Big Data. Researchers should be familiar with the strengths and weaknesses of current implementations of GBDT's in order to use them effectively and make successful contributions. CatBoost is a member of the family of GBDT machine learning ensemble techniques. Since its debut in late 2018, researchers have successfully used CatBoost for machine learning studies involving Big Data. We take this opportunity to review recent research on CatBoost as it relates to Big Data, and learn best practices from studies that cast CatBoost in a positive light, as well as studies where CatBoost does not outshine other techniques, since we can learn lessons from both types of scenarios. Furthermore, as a Decision Tree based algorithm, CatBoost is well-suited to machine learning tasks involving categorical, heterogeneous data. Recent work across multiple disciplines illustrates CatBoost's effectiveness and shortcomings in classification and regression tasks. Another important issue we expose in literature on CatBoost is its sensitivity to hyper-parameters and the importance of hyper-parameter tuning. One contribution we make is to take an interdisciplinary approach to cover studies related to CatBoost in a single work. This provides researchers an in-depth understanding to help clarify proper application of CatBoost in solving problems. To the best of our knowledge, this is the first survey that studies all works related to CatBoost in a single publication.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.