Hello all, I'm working mostly with machine learning code in R and looking for a structured way to check if my code is working properly. For example if I train a classifier on some data. How do I know if the good / bad results are related to the data are not just an programming error that I introduced somewhere. results are to good: I might have used some part of the test data for training results are to bad: could have any reason I know that I can in principle generate data containing no information at all or pure information to benchmark my code but is there a more elaborate or easyer way to that? I guess what I'm basically looking for is some kind of unit testing framework to generate test data for machine learning tasks, I read about the package RUnit but don't really know how to proceed from there. Any ideas? How do you test your data analysis code? best regards, Immanuel