In this section, we will explore grid searches.
We'll talk a bit about optimization versus grid searching, setting up a model generator function, setting up a parameter grid and doing a grid search with cross-validation, and finally, reporting the outcomes of our grid search so we can pick the best model.
So why, fundamentally, are there two different kinds of machine learning activities here? Well, optimization solves for parameters with feedback from a loss function: it's highly optimized. Specifically, a solver doesn't have to try every parameter value in order to work. It uses a mathematical relationship with partial derivatives in order to move along what is called a gradient. This lets it go essentially downhill mathematically ...
