GitHub - benearnthof/geocomputation: An analysis of spatial data with various machine learning classifiers

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An analysis of spatial data with various machine learning classifiers

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Name		Name	Last commit message	Last commit date
Latest commit History 5 Commits
Daten		Daten
FinalReport		FinalReport
.gitignore		.gitignore
DEU_msk_alt.grd		DEU_msk_alt.grd
DEU_msk_alt.gri		DEU_msk_alt.gri
DEU_msk_alt.vrt		DEU_msk_alt.vrt
app.R		app.R
app_imports.R		app_imports.R
basefit.RDS		basefit.RDS
gadm36_DEU_1_sp.rds		gadm36_DEU_1_sp.rds
geocomp_notes.R		geocomp_notes.R
geocomputation.Rproj		geocomputation.Rproj
header_pagenrs.tex		header_pagenrs.tex
ironagesites.R		ironagesites.R
ksvmpred.rds		ksvmpred.rds
packageloader.R		packageloader.R
predictorstack.png		predictorstack.png
preprocessing.R		preprocessing.R
presentation.Rmd		presentation.Rmd
presentationimports.R		presentationimports.R
readme.R		readme.R
rftune1.rds		rftune1.rds
riverdist_endeavour.R		riverdist_endeavour.R
script.R		script.R
slides.css		slides.css
spatial_bootstrap.R		spatial_bootstrap.R

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# readme

# chapter 11: reproducing results of chapter 11 to understand MLR building blocks
# chapter 14: reproducing results of chapter 14 to understand interfaces to GIS software => SAGA
# geocomp_notes: working through basically the whole book and taking notes
# ironagesites: applying what I have learned to a different dataset
# packageloader: a nifty script i wrote to automatically install and load packages
# script: can be ignored - will be deleted later on
# spatial_bootstrap: a simulation to compare different resampling methods for sptially correlated data
# testfile: notes and sources
# todo: a todo list
# readme: you're currently reading this one