I'm asking this out of curiosity.
In the past I have thought of an heuristic as a "quick and dirty" rule not based on data analysis, as opposed to a solution which uses machine learning or statistical models.
For example imagine I have the following classification problem: are products listed in different e-commerce websites the same physical product?
One could simply define an initial heuristic that if product titles similarity is below an arbitrary level than products are considered different otherwise they are considered the same. This would be what I call an heuristic.
On the other hand, tagging data, training a model and cross validate to get best threshold to classify products is NOT an heuristic.
However if I read Wikipedia definition of Heuristic it says that it is a practical method that does not guarantee to be optimal or perfect. This seems very general and it seems that it could be extended to machine learning.
Can anyone help me understand the distinction a bit better?