In everyday use a model is a system which explains the behavior of some system, hopefully at the level where some alteration of the model predicts some alteration of the real-world system. In machine learning “model” has several variant definitions.
- Everyday. The common definition is sometimes used.
- Parameterized. Sometimes model is a short-hand for “parameterized model”. Here, it refers to a model with unspecified free parameters. In the Bayesian learning approach, you typically have a prior over (everyday) models.
- Predictive. Even further from everyday use is the predictive model. Examples of this are “my model is a decision tree” or “my model is a support vector machine”. Here, there is no real sense in which an SVM explains the underlying process. For example, an SVM tells us nothing in particular about how alterations to the real-world system would create a change.
Which definition is being used at any particular time is important information. For example, if it’s a parameterized or predictive model, this implies some learning is required. If it’s a predictive model, then the set of operations which can be done to the model are restricted with respect to everyday usage. I don’t have any particular advice here other than “watch out”—be aware of the distinctions, watch for this source of ambiguity, and clarify when necessary.