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Hello, it looks like the Auto model is designed to extract interleaved training and test sets at a ratio of 0.6 to 0.4 over the whole example set range. The model gives then a very good regression with my dataset.
Creating the Model based on training and testing data sets using linear sampling (0.9 -0.1) resulted in an about 4 times worse performance. This indicates that the model needs further steps to get more generalized and the importance of the training set preparation.
Thus, it would be still nice to have a choice for data set splitting in the Auto model.
In addition, the problem remains how to further optimize the Model to get more generalized. One way could be to run the Model using a variety of data set splitting to optimize the Model parameters or to add random noise levels into the data, like in Image recognition approaches.
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