Madlene Nussbaum: Mastering ML for spatial prediction I - overview and introduction in methods
Channel: Tomislav Hengl (OpenGeoHub Foundation)
Date: 16/10/2019

Many algorithm driven statistical methods are nowadays used for (spatial) prediction. Participants will get an overview of the different types/families of methods (shrinkage, generalized additive models, tree based methods, neural networks, support vector machines) and different machine learning concepts (bootstrap, boosting, model averaging). Three methods selected from different families (random forest, support vector machines, lasso) are presented in more detail including tuning of model parameters for these models.

Installation instructions
You need R and an Editor, e.g. RStudio
Following packages, run code:
install.packages(c("grpgreg", "glmnet", "kernlab", "caret", "randomForest", "mboost",
                   "gbm", "geoGAM", "raster", "quantregForest"))

Documents
https://github.com/mnocci/2019_OpenGeoHub_machine-learning-madlene