Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data.

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 <strong>pathogen-modeling-3.zip </strong>or can be found directly in the Github repository. <strong>Publication figures</strong>: analysis/paper/submission/3/latex-source-files/ <strong>Appendices</strong>: 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. <strong>Data sources</strong> Atlas Climatico: http://opengis.uab.es/wms/iberia/index.htm DEM: ftp://ftp.geo.euskadi.eus/lidar/MDE_LIDAR_2016_ETRS89/ Lithology: http://www.geo.euskadi.eus/geonetwork/srv/spa/main.home pH: https://esdac.jrc.ec.europa.eu/content/soil-ph-europe#tabs-0-description=0 soil: https://www.isric.org/explore/soilgrids <strong>Licenses</strong> All files are shared via the given license with the exception of "soil.tif" which is shared via the <strong>ODbL </strong>license<strong>.</strong>