[dceddia]

As someone who's starting to learn a bit about machine learning, it feels like the whole field is full of fancy terms like this that seem to mostly map to simpler or more familiar ones. "linear regression" instead of fitting a line, "hyperparameter" instead of user-provided argument. Half the battle seems to be building this mental translation map.

[Test0129]

You are looking at it from a programmer standpoint rather than a mathematical standpoint.

Linear regression isn't just fitting a line, it's a statistical technique to fit a line of best fit. Hyperparameters are a bayesian term for parameters outside the system of test or "algorithm". User input really misses the bayesian aspect.

These terms actually have meaning so I'd be careful ascribe simpler definitions. The underlying meaning is important to the reason they work. If you don't have a really strong background in probability theory and statistics trying to dig into machine learning will take work. Id recommend taking an MITx course or picking up a textbook on probability so the terminology feels more natural.

[Sharlin]

To be fair, "linear regression" is standard statistics 101 that much predates machine learning or computers.

[meowkit]

A user-provided argument could also be an input parameter or a regular function parameter altogether.

Yes, hyperparameters are often set by the user of a model, but more specifically they are parameters that exist separately from the data put into a model (input parameters) or the structure inside of neural networks (hidden parameters). Hyper- meaning above, helps conceptualize these parameters as existing outside the model.