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I tried something extreme, and the results were too: I generated weather data with solar orientations, tilts, wind directions, ... in total about 1600 variables which resulted in this formula:
I could obviously fix it by reducing the number of variables. But what might also work is this: define certain "families" of variables (for instance, the heating degree days), and make sure the Analysis only uses 1 of them to make its model.
Could just be a list of lists, like
This is exactly what @kdebrab mentioned yesterday: with lots of potential dependent variables, you will get a perfect model (R²=1).
Can you post the fit.summary() of the result? I want to have a look at model statistics.
The list-of-list approach to create groups of dependent variables should work, but could again lead to an overfitted model. So preferentially, I'd like to find a way to avoid overfitting in general, without imposing any limits on the combination of variables.
I tried something extreme, and the results were too: I generated weather data with solar orientations, tilts, wind directions, ... in total about 1600 variables which resulted in this formula:
And got a miraculous RSquared of 1!
I could obviously fix it by reducing the number of variables. But what might also work is this: define certain "families" of variables (for instance, the heating degree days), and make sure the Analysis only uses 1 of them to make its model.
Could just be a list of lists, like
@saroele thoughs?
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