This is a bit of a rant about the use of the word *model*:
> ... ‘machine learning models’, but could equally have used ‘statistical models’.
> a model is any systematic procedure ... providing a prediction
I don't like this usage of the word model. I don't think it makes sense to call a procedure a model. You are stretching the word, so that includes the method of inference/estimation along with what I think should be called a model. You are far from the only person who does this, and maybe its too late to do anything about it, but I really wish people wouldn't!
[Wikipedia](http://en.wikipedia.org/wiki/Model_%28disambiguation%29) has several meanings for the word, including:
* Model (economics), a theoretical construct representing economic processes
* Model (physical), a smaller or larger physical copy of an object
* Scale model, a replica or prototype of an object
* Computer model, a simulation to reproduce behavior of a system
* Conceptual model (computer science), representation of entities and relationships between them
* Mathematical model, a description of a system using mathematical concepts and language
* Statistical model, in applied statistics, a parameterized set of probability distributions
They're all fine. So is [animal model](http://en.wikipedia.org/wiki/Animal_model). They are all types of stand-in for the real thing. But they are not procedures for prediction. They can be used for making predictions, but that is a separate step. It becomes particularly confusing when you have a stochastic model (a better name than statistical model) and an estimation method (maximum likelihood, Bayesian posterior mean, etc) and people call the whole thing a model. For example, "maximum likelihood model" is used in this Nature paper [Reconstructing the early evolution of Fungi using a six-gene phylogeny](http://curis.ku.dk/ws/files/33289644/james_et_al.pdf) to refer to a model (in the correct sense) of evolution, *and* a particular statistical procedure.
As an alternative I suggest *machine*.