Just visualizing for a couple of non-linear models how the predictions develop, when confronted with sample data out of the trained distribution. The modeling methods are:
- Support Vector Regression
- Random Forrest
- Gradient Booster
- Simple Feed Forward Network
- Regression Tree
- Gaussean Process
The data is some arbitrary trigonometric surface in 3d space.