CAST: 'caret' Applications for Spatial-Temporal Models

CASTR Documentation

'caret' Applications for Spatial-Temporal Models

Description

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial-temporal modelling tasks using 'caret'. It supports Leave-Location-Out and Leave-Time-Out cross-validation of spatial and spatial-temporal models and allows for spatial variable selection to selects suitable predictor variables in view to their contribution to the spatial model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models by analysing the similarity between new data and training data.

Details

'caret' Applications for Spatio-Temporal models

Author(s)

Hanna Meyer, Marvin Ludwig

References

  • Meyer, H., Pebesma, E. (2022):Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications. Accepted.

  • Meyer, H., Pebesma, E. (2021): Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution. 12, 1620– 1633.

  • Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411, 108815.

  • Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauß, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software 101: 1-9.


CAST documentation built on March 18, 2022, 5:28 p.m.