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Assessing the performance of spatial cross-validation approaches for
  models of spatially structured data

Assessing the performance of spatial cross-validation approaches for models of spatially structured data

13 March 2023
M. Mahoney
L. Johnson
Julia Silge
Hannah Frick
Max Kuhn
Colin M. Beier
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Papers citing "Assessing the performance of spatial cross-validation approaches for models of spatially structured data"

3 / 3 papers shown
Title
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning
  in R
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R
P. Schratz
Marc Becker
Michel Lang
A. Brenning
28
6
0
25 Oct 2021
Importance of spatial predictor variable selection in machine learning
  applications -- Moving from data reproduction to spatial prediction
Importance of spatial predictor variable selection in machine learning applications -- Moving from data reproduction to spatial prediction
H. Meyer
C. Reudenbach
Stephan Wöllauer
T. Nauss
51
289
0
21 Aug 2019
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
93
2,732
0
18 Aug 2015
1