I'm trying to setup my first workflow with regression modeling. I've generated a data set containing four input variables (x, y, z, k) and one output variable. x, y, z take values in the interval between 0 and 2 and k is randomly set to 0 or 1.
The output variable was computed as: x^2 + 2*y + z^(0.5) + k.
I'm trying then to do a basic polynomial regression analysis. I've attached a screenshot of the workflow: data is partitioned, the first part is used for the learner, the second part is used to test the predictor.
If I'm using a maximum polynomial degree of 1, it works with reasonable errors. If I'm trying to use higher polynomial degrees however, I get the following error:
ERROR Polynomial Regression (Learner) Execute failed: The attributes of the data samples are not mutually independent.
Can someone please tell me which could be the cause. I've observed, that if I exclude input variable 'k' from the analysis, it works fine for any polynomial degree.