In general:

- a model takes an input x and maps it to an output y
- an algorithm is a method or a process that can be defined with certain steps, e.g. like gradient descent. The algorithm can terminate when certain quality criteria is reached, e.g. when an optimum is reached resp. a condition is fulfilled.

My take on that particular question:

- So a linear regression w/ y=f(x) = m x + b is a statistical
**model**since it transforms the input x linearly to the model output y - More specifically: the way you
**fit the model**when the models parameters are learned can be described with an algorithm, e.g . Gradient descent in the context of linear regression:

Summary:

In my opinion it depends on the context here on what you want to address:

- the fitting process (then the term algorithm is totally fine)
- or if you mean the (parametrized) model that maps an input to an output. Then you better call it just a regression model.

Hope that helps, @mvrbiguv! Please let me know if anything is unclear.

Side note: if you want to check out how linear regression can be solved with the normal equation, this thread might be worth a look.

Best regards

Christian