Summary of The Cast Package For Training and Assessment Of Spatial Prediction Models in R, by Hanna Meyer et al.
The CAST package for training and assessment of spatial prediction models in R
by Hanna Meyer, Marvin Ludwig, Carles Milà, Jan Linnenbrink, Fabian Schumacher
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Machine learning algorithms are widely used in environmental science to map environmental variables continuously in space or even in space and time. This paper focuses on the task of making spatial predictions by estimating the value of a variable of interest in places where it has not been measured, based on local field observations. However, this task involves additional challenges compared to non-spatial prediction tasks, including spatial autocorrelation and dependence between training data points. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Environmental scientists use machine learning to predict environmental variables like never before! They take measurements from one place and use them to figure out what’s happening in other places. But it’s not as simple as just using the information they have – there are some special challenges that come with making predictions about things that happen at different locations. |
Keywords
» Artificial intelligence » Machine learning
