Hyperparameter tuning and performance assessment of statistical and machine-learning models using spatial data. (Q6676444)

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-3.zipor 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.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 Licenses All files are shared via the given license with the exception of soil.tif which is shared via theODbL license.