What is 'parameter convergence'? I'm trying to teach myself data science, with my particular interest being decision trees. A few steps in, I've come across a term, 'parameter convergence' that I can't find a definition for (because, after all, I'm learning on my own and have no access to teachers or peers):


  However, even in studies with much lower numbers of predictor
  variables, the combination of all main and interaction effects of
  interest – especially in the case of categorical predictor variables –
  may well lead to cell counts too sparse for parameter convergence. (from Strobl et al., 2009)


A web search isn't overly helpful because convergence is such a common term, and I'm not sure which results specifically apply in the context of decision trees. And also, the results don't provide an entry-level definition.

So, while a definition or explanation of parameter convergence (in the context of recursive partitioning) would be great, it would also be handy to be directed to a resource (academic or otherwise) that might have a 'glossary' of this and similar terms...
A naive definition of Parameter convergence is when the weights or the values of the parameters reach a point asymptotically. What I mean is that, when your model training is not altering the parameter values(maybe less than epsilon-small values) it might be a good fit. For decision trees, I found this paper which explains rate of convergence and more. It might be a good read if you want to get more details.