For a recent research project we used machine-learning. In the preprocessing phase we removed 2 predictors because they contained mostly uninformative 0 values (x1 = 100%, x2 = 99%). Is it the right terminology to call them invariant?
My main justification for the exclusion is, that it reduces the degrees of freedom and therefore the risk of overfitting.
I am looking for a paper / source to justify this procedure, besides the face validity of doing so. Do you have a recommendation for such a source?
Thanks to mkt:
I found what I was looking for in this book: Kuhn, M., & Johnson, K. (2013). Applied predictive modeling. Applied Predictive Modeling (Vol. 26). New York: Springer. https://doi.org/10.1007/978-1-4614-6849-3