This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data". The code (including figures, appendices and the manuscript) is packed in pathogen-modeling-master.zip or can be found directly in the Github repository. Publication figures: analysis/paper/submission/3/latex-source-files/ Appendices: analysis/paper/submission/3/ This RC represents a static snapshot at the time of submission. The Github repository will receive changes after the publication was published. Data sources Atlas Climatico: http://opengis.uab.es/wms/iberia/index.htm DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/ Lithology: http://www.ge...
Computational and statistical prediction methods such as the support vector machine have gained popu...
This dataset contains the data, documentation, and scripts that compose the SPATIAL SCALING CHALLENG...
Hierarchical spatial modelling is useful for modelling complex spatially correlated data in a variet...
This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assess...
This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assess...
This repository will contain the research compendium of our work on comparing algorithms across diff...
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeli...
Data for the article "Crucial but often neglected: The important role of spatial autocorrelation in ...
Selecting the best configuration of hyperparameter values for a Machine Learning model yields direct...
I investigate, using the R package spaMM, the effect of misspecification of the smoothing parameter,...
Spatial and spatiotemporal machine-learning models require a suitable framework for their model asse...
Iterative Proportional Fitting (IPF), also known as biproportional fitting, ‘raking’ or the RAS algo...
Many physical quantities around us vary across space or space-time. An example of a spatial quantity...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
This project focuses on the concept of hyperparameters in a Machine Learning classifi- cation proble...
Computational and statistical prediction methods such as the support vector machine have gained popu...
This dataset contains the data, documentation, and scripts that compose the SPATIAL SCALING CHALLENG...
Hierarchical spatial modelling is useful for modelling complex spatially correlated data in a variet...
This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assess...
This is a research compendium (RC) for the publication "Hyperparameter tuning and performance assess...
This repository will contain the research compendium of our work on comparing algorithms across diff...
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeli...
Data for the article "Crucial but often neglected: The important role of spatial autocorrelation in ...
Selecting the best configuration of hyperparameter values for a Machine Learning model yields direct...
I investigate, using the R package spaMM, the effect of misspecification of the smoothing parameter,...
Spatial and spatiotemporal machine-learning models require a suitable framework for their model asse...
Iterative Proportional Fitting (IPF), also known as biproportional fitting, ‘raking’ or the RAS algo...
Many physical quantities around us vary across space or space-time. An example of a spatial quantity...
We give an overview of the papers published in this special issue on spatial statistics, of the Jour...
This project focuses on the concept of hyperparameters in a Machine Learning classifi- cation proble...
Computational and statistical prediction methods such as the support vector machine have gained popu...
This dataset contains the data, documentation, and scripts that compose the SPATIAL SCALING CHALLENG...
Hierarchical spatial modelling is useful for modelling complex spatially correlated data in a variet...