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Benchmarking and Optimization of Gradient Boosting Decision Tree
  Algorithms
v1v2v3 (latest)

Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms

12 September 2018
Andreea Anghel
N. Papandreou
Thomas Parnell
Alessandro De Palma
H. Pozidis
ArXiv (abs)PDFHTML

Papers citing "Benchmarking and Optimization of Gradient Boosting Decision Tree Algorithms"

9 / 9 papers shown
Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
Nikolaos Pavlidis
V. Perifanis
Symeon Symeonidis
P. Efraimidis
164
0
0
24 Aug 2025
dnamite: A Python Package for Neural Additive Models
dnamite: A Python Package for Neural Additive Models
Mike Van Ness
Madeleine Udell
195
0
0
06 Mar 2025
Machine Learning For An Explainable Cost Prediction of Medical Insurance
Machine Learning For An Explainable Cost Prediction of Medical InsuranceMachine Learning with Applications (MLWA), 2023
U. Orji
Elochukwu A. Ukwandu
355
55
0
23 Nov 2023
JoinBoost: Grow Trees Over Normalized Data Using Only SQL
JoinBoost: Grow Trees Over Normalized Data Using Only SQLProceedings of the VLDB Endowment (PVLDB), 2023
Zezhou Huang
Rathijit Sen
Jiaxiang Liu
Eugene Wu
150
21
0
01 Jul 2023
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes
  Heterogeneous Feature Interaction
T2G-Former: Organizing Tabular Features into Relation Graphs Promotes Heterogeneous Feature InteractionAAAI Conference on Artificial Intelligence (AAAI), 2022
Jiahuan Yan
Jintai Chen
YiXuan Wu
Benlin Liu
Jian Wu
303
58
0
30 Nov 2022
DANets: Deep Abstract Networks for Tabular Data Classification and
  Regression
DANets: Deep Abstract Networks for Tabular Data Classification and RegressionAAAI Conference on Artificial Intelligence (AAAI), 2021
Jintai Chen
Kuan-Yu Liao
Yao Wan
Benlin Liu
Jian Wu
LMTD
372
79
0
06 Dec 2021
Light Gradient Boosting Machine as a Regression Method for Quantitative
  Structure-Activity Relationships
Light Gradient Boosting Machine as a Regression Method for Quantitative Structure-Activity Relationships
R. Sheridan
Andy Liaw
M. Tudor
AI4CE
116
15
0
28 Apr 2021
Interpretable Machine Learning Models for Predicting and Explaining
  Vehicle Fuel Consumption Anomalies
Interpretable Machine Learning Models for Predicting and Explaining Vehicle Fuel Consumption AnomaliesEngineering applications of artificial intelligence (EAAI), 2020
A. Barbado
Óscar Corcho
425
15
0
28 Oct 2020
Neural Oblivious Decision Ensembles for Deep Learning on Tabular Data
Neural Oblivious Decision Ensembles for Deep Learning on Tabular DataInternational Conference on Learning Representations (ICLR), 2019
Sergei Popov
S. Morozov
Artem Babenko
LMTD
495
397
0
13 Sep 2019
1
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