XGBoost is a research paper published in Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2016). On theSindex it has a DataRank of 1.6. It has been cited 46,442 times.
Scored on demand from live citation data
Linked data & code
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
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Base Score Contribution
1.6
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →Office of Naval Research
Grant: N000141010672
National Science Foundation
Grant: 1258741
RI: Small: GraphLab 2: An Abstraction and System for Large-Scale Parallel Machine Learning on Natural Graphs
FWCI
528.31
Citation Percentile
1.0%
Citation Trend
Fields of Study
Keywords
Sustainable Development Goals
Рецензия на статью: Моделирование стоимости жилой недвижимости Московской области с использованием методов машинного обучения
Рецензия на статью: Моделирование стоимости жилой недвижимости Московской области с использованием методов машинного обучения
Результаты исследования
Методы моделирования стоимости недвижимости
Пространственные кластеры стоимости жилой недвижимости Московской области
Распределение цен на жилую недвижимость
Влияние признаков на прогноз стоимости недвижимости
Матрица корреляции признаков
Моделирование стоимости жилой недвижимости Московской области с использованием методов машинного обучения
Efficient Explanations for Rule Ensembles
Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions (Artifact)
Uncertainty-Aware Resource Allocation for Multi-Path Programs with In-Kernel Predictions
On DoS Attacks Exploiting Input Representativeness in Mixed-Criticality Systems
Different Scales of Randomness: Empirical Mixing Times of the Edge Switching and Curveball MCMC
D-GRIL: End-To-End Topological Learning with 2-Parameter Persistence
A Two-Stage, Leakage-Aware Framework for Early Academic Risk Detection in Undergraduate Engineering Cohorts
A Two-Stage, Leakage-Aware Framework for Early Academic Risk Detection in Undergraduate Engineering Cohorts
SwiftQueue: Optimizing Low-Latency Applications with Swift Packet Queuing
Additional file 1 of Machine learning algorithm for early detection of end-stage renal disease
Additional file 1 of Machine learning algorithm for early detection of end-stage renal disease