Search Results for author:
Found 5 papers, 4 papers with code
Web scraping: a promising tool for geographic data acquisition
With much of our lives taking place online, researchers are increasingly turning to information from the World Wide Web to gain insights into geographic patterns and processes.
Spatial machine-learning model diagnostics: a model-agnostic distance-based approach
While significant progress has been made towards explaining black-box machine-learning (ML) models, there is still a distinct lack of diagnostic tools that elucidate the spatial behaviour of ML models in terms of predictive skill and variable importance.
mlr3spatiotempcv: Spatiotemporal resampling methods for machine learning in R
This contribution reviews the state-of-the-art in spatial and spatiotemporal cross-validation, and introduces the {R} package {mlr3spatiotempcv} as an extension package of the machine-learning framework {mlr3}.
Transforming Feature Space to Interpret Machine Learning Models
Model-agnostic tools for interpreting machine-learning models struggle to summarize the joint effects of strongly dependent features in high-dimensional feature spaces, which play an important role in pattern recognition, for example in remote sensing of landcover.
Performance evaluation and hyperparameter tuning of statistical and machine-learning models using spatial data
Results show that GAM and RF (mean AUROC estimates 0. 708 and 0. 699) outperform all other methods in predictive accuracy.
