I really recommend reading this (longish) post by Tom Breur called “Data Dredging” (and following his blog. The post is dedicated to overfitting — the most scaring problem in machine learning. Overfitting is easy to do and is hard to avoid. It is a serious problem when working with “small data” but is also a problem in the big data era. Read “Data Dredging” for an overview of the problem and its possible cures.
Quoting Tom Breur:
Reality for many data scientist is that the data at hand, in particular some minority class you are predicting, are almost always in short supply. You would like to have more data, but they simply aren’t available. Still, there might be excellent business value in building the best possible model from these data, as long as you safeguard against overfitting. Happy dredging!
Happy dredging indeed.
Boris Gorelik 