For the training of a machine learning model I need to add additional features, and these features are correlated. I need to run the model N times adding these features with random values, and for this I use Cholesky decomposition.
Now, I need the user to define these features in ranges, for example:
Feature 1 between 1000 and 2000 Feature 2 between 3 and 5 Features 3 between 20 and 30
With the Cholesky decomposition I can take one of the features and get the others.
But what I need is the system to calculate randomly N sets of random features, within the ranges. So the input is not one of the features, but all of them.
If the ranges provided don't make sense (i.e. there's no way to use the correlation to get a set within the ranges), then the process should fail.
Is there a way to accomplish this (either with or without Cholesky)?