Hyperparameter tuning

Looking at bit into MLJ integration. For better or worse, hyper-parameter optimization (eg, grid search) in MLJ generally works by mutating the field values of the model struct. I wonder if TableTransforms.jl would consider changing their transformer types to mutable structs? I think in ML applications, at least, any loss in performance would be pretty minimal, but perhaps there are wider use-cases to consider?

The alternative for our use case is for the MLJ model *wrapper* to be mutable (for now a wrapper is necessary anyway) and that a user wanting to do a search does something like

```julia
values = [Scale(low=0, high=x) for x in 1.0:0.1:10] <---- extra step
values = range(wrapped_transformer, :model, values=values)
```

However, while this might be fine for Grid search, it doesn't really work for other optimization strategies.

Thoughts?