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LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping
v1v2 (latest)

LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping

27 February 2025
J. Schmidinger
S. Vogel
V. Barkov
A.-D. Pham
R. Gebbers
H. Tavakoli
J. Correa
T. R. Tavares
P. Filippi
E. J. Jones
V. Lukas
E. Boenecke
J. Ruehlmann
I. Schroeter
E. Kramer
S. Paetzold
M. Kodaira
A. M. J.-C. Wadoux
L. Bragazza
K. Metzger
J. Huang
D. S. M. Valente
J. L. Safanelli
E. L. Bottega
R. S. D. Dalmolin
C. Farkas
A. Steiger
T. Z. Horst
L. Ramirez-Lopez
T. Scholten
F. Stumpf
P. Rosso
M. M. Costa
R. S. Zandonadi
J. Wetterlind
M. Atzmueller
ArXiv (abs)PDFHTMLGithub (3★)

Papers citing "LimeSoDa: A Dataset Collection for Benchmarking of Machine Learning Regressors in Digital Soil Mapping"

11 / 11 papers shown
Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Kriging prior Regression: A Case for Kriging-Based Spatial Features with TabPFN in Soil Mapping
Jonas Schmidinger
Viacheslav Barkov
Sebastian Vogel
Martin Atzmueller
Gerard B M Heuvelink
214
5
0
11 Sep 2025
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Modern Neural Networks for Small Tabular Datasets: The New Default for Field-Scale Digital Soil Mapping?
Viacheslav Barkov
Jonas Schmidinger
Robin Gebbers
Martin Atzmueller
135
2
0
13 Aug 2025
An Efficient Model-Agnostic Approach for Uncertainty Estimation in
  Data-Restricted Pedometric Applications
An Efficient Model-Agnostic Approach for Uncertainty Estimation in Data-Restricted Pedometric ApplicationsInternational Conference on Machine Learning and Applications (ICMLA), 2024
Viacheslav Barkov
Jonas Schmidinger
Robin Gebbers
Martin Atzmueller
245
3
0
18 Sep 2024
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse
  Tabular Datasets
A Comprehensive Benchmark of Machine and Deep Learning Across Diverse Tabular Datasets
Assaf Shmuel
Oren Glickman
Teddy Lazebnik
LMTD
225
18
0
27 Aug 2024
When Do Neural Nets Outperform Boosted Trees on Tabular Data?
When Do Neural Nets Outperform Boosted Trees on Tabular Data?Neural Information Processing Systems (NeurIPS), 2023
Duncan C. McElfresh
Sujay Khandagale
Jonathan Valverde
C. VishakPrasad
Ben Feuer
Chinmay Hegde
Ganesh Ramakrishnan
Micah Goldblum
Colin White
LMTD
433
293
0
04 May 2023
Why do tree-based models still outperform deep learning on tabular data?
Why do tree-based models still outperform deep learning on tabular data?
Léo Grinsztajn
Edouard Oyallon
Gaël Varoquaux
LMTD
438
539
0
18 Jul 2022
Leakage and the Reproducibility Crisis in ML-based Science
Leakage and the Reproducibility Crisis in ML-based Science
Sayash Kapoor
Arvind Narayanan
210
225
0
14 Jul 2022
Tabular Data: Deep Learning is Not All You Need
Tabular Data: Deep Learning is Not All You NeedInformation Fusion (Inf. Fusion), 2021
Ravid Shwartz-Ziv
Amitai Armon
LMTD
481
1,881
0
06 Jun 2021
TUDataset: A collection of benchmark datasets for learning with graphs
TUDataset: A collection of benchmark datasets for learning with graphs
Christopher Morris
Nils M. Kriege
Franka Bause
Kristian Kersting
Petra Mutzel
Marion Neumann
725
1,035
0
16 Jul 2020
Improving Reproducibility in Machine Learning Research (A Report from
  the NeurIPS 2019 Reproducibility Program)
Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)Journal of machine learning research (JMLR), 2020
Joelle Pineau
Philippe Vincent-Lamarre
Koustuv Sinha
V. Larivière
A. Beygelzimer
Florence dÁlché-Buc
E. Fox
Hugo Larochelle
591
504
0
27 Mar 2020
ranger: A Fast Implementation of Random Forests for High Dimensional
  Data in C++ and R
ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R
Marvin N. Wright
A. Ziegler
611
3,342
0
18 Aug 2015
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