Tuning strategy components (#73)

* Tuning strategy components

I think it what a tuning strategy entails could be stated explicitly

* Update A-model-tuning.jl

Incorporating @abloam's suggestion for the description of the tuning strategy.

* Update A-model-tuning.jl

* Update A-model-tuning.jl

Co-authored-by: gd1989 <[email protected]>
Co-authored-by: Thibaut Lienart <[email protected]>

Author: 3 people

_literate/A-model-tuning.jl

@@ -27,10 +27,18 @@ r   = range(dtc, :max_depth, lower=1, upper=5)
 # For hyperparameters of type `<:Real`, you should specify a range of values as done above.
 # For hyperparameters of other type (e.g. `Symbol`), you should use the `values=...` keyword.
 #
-# Once a range of values has been defined, you can then wrap the model in a `TunedModel` specifying the tuning strategy:
+# Once a range of values has been defined, you can then wrap the model in a `TunedModel` specifying the tuning strategy.
 
 tm = TunedModel(model=dtc, ranges=[r, ], measure=cross_entropy)
 
+# Note that "wrapping a model in a tuning strategy" as above means creating a new "self-tuning" version of the model, `tuned_model = TunedModel(model=...)`, in which further key-word arguments specify:
+# 1. the algorithm (a.k.a., tuning strategy) for searching the hyper-parameter space of the model (e.g., `tuning = Random(rng=123)` or `tuning = Grid(goal=100)`).
+# 2. the resampling strategy, used to evaluate performance for each value of the hyper-parameters (e.g., `resampling=CV(nfolds=9, rng=123)` or `resampling=Holdout(fraction_train=0.7)`).
+# 3. the measure (or measures) on which to base performance evaluations (and for reporting purposes) (e.g., `measure = rms` or `measures = [rms, mae]`).
+# 4. the range, usually describing the "space" of hyperparameters to be searched (but more generally whatever extra information is required to complete the search specification, e.g., initial values in gradient-descent optimization).
+
+# For more options do `?TunedModel`.
+
 # ### Fitting and inspecting a tuned model
 #
 # To fit a tuned model, you can use the usual syntax: