forcing variables to regression

[geek-mr-xie]

i have a function like f(x,y).
but the regression result only have f(y).
the MSE is about 1.0 of 6000 points sample.

[MilesCranmer]

If you only have two variables, then 6000 points is overkill. I would try with ~100 points. It is likely the equations are taking too long to evaluate and it simply has not tried very many. Alternatively, (1) (only if you are using the PySR frontend; not available in Julia) if your data is very noisy, which is why you are using 6000, you could turn on denoising with (denoise=True). If you are doing this, you would want to pass Xresampled as an array of random ~100 points within the domain of your data. Xresampled = np.random.rand(100, 2) Xresampled[:, 0] = Xresampled[:, 0] * (xmax - xmin) + xmin Xresampled[:, 1] = Xresampled[:, 1] * (ymax - ymin) + ymin model = PySRRegressor(..., denoise=True, Xresampled=Xresampled) (2) You could try turning on batching (batching=True), and set some batchSize to some small number. This way it will still evaluate on all 6000 points at the final stages, but will evaluate on a random selection during the search.

[geek-mr-xie]

Thanks for your patient reply. I am trying to run my julia script again. The variable x back to my formular.
But the MSE is still around 1.0.

[geek-mr-xie]

Your (2) is very useful, by setting the batching true my x back again.

[MilesCranmer]

Awesome, glad to hear it works.