revisit docs on model formulas (#1022)

Author: simonpcouch

man/details_boost_tree_xgboost.Rd

@@ -60,7 +60,7 @@ gen_additive_mod() %>%
 The smoothness of the terms will need to be manually specified (e.g., using `s(x, df = 10)`) in the formula. Tuning can be accomplished using the `adjust_deg_free` parameter. 
 
 
-However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula:
+When using a workflow, pass the _model formula_ to [add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
 
 ```{r}
 spec <- 
@@ -69,13 +69,13 @@ spec <-
   set_mode("regression")
 
 workflow() %>% 
-  add_variables(outcomes = c(mpg), predictors = c(wt, gear, cyl, disp)) %>% 
   add_model(spec, formula = mpg ~ wt + gear + cyl + s(disp, k = 10)) %>% 
+  add_formula(mpg ~ wt + gear + cyl + disp) %>% 
   fit(data = mtcars) %>% 
   extract_fit_engine()
 ```
 
-The reason for this is that [workflows::add_formula()] will try to create the model matrix and fail to find/use `s()`.  
+To learn more about the differences between these formulas, see [`?model_formula`][parsnip::model_formula].
 
 ## Preprocessing requirements
 

man/details_gen_additive_mod_mgcv.Rd

@@ -96,7 +96,7 @@ gen_additive_mod() %>%
 The smoothness of the terms will need to be manually specified (e.g., using `s(x, df = 10)`) in the formula. Tuning can be accomplished using the `adjust_deg_free` parameter. 
 
 
-However, when using a workflow, the best approach is to avoid using [workflows::add_formula()] and use [workflows::add_variables()] in conjunction with a model formula:
+When using a workflow, pass the _model formula_ to [add_model()]'s `formula` argument, and a simplified _preprocessing formula_ elsewhere.
 
 
 ```r
@@ -106,8 +106,8 @@ spec <-
   set_mode("regression")
 
 workflow() %>% 
-  add_variables(outcomes = c(mpg), predictors = c(wt, gear, cyl, disp)) %>% 
   add_model(spec, formula = mpg ~ wt + gear + cyl + s(disp, k = 10)) %>% 
+  add_formula(mpg ~ wt + gear + cyl + disp) %>% 
   fit(data = mtcars) %>% 
   extract_fit_engine()
 ```
@@ -126,7 +126,7 @@ workflow() %>%
 ## GCV score: 4.225228
 ```
 
-The reason for this is that [workflows::add_formula()] will try to create the model matrix and fail to find/use `s()`.  
+To learn more about the differences between these formulas, see [`?model_formula`][parsnip::model_formula].
 
 ## Preprocessing requirements
 

man/details_mlp_brulee.Rd

@@ -169,7 +169,7 @@ tidy(fit)
 ## 4 hp           -0.0101       1
 ## 5 drat          0            1
 ## 6 wt           -2.59         1
-## # … with 5 more rows
+## # ℹ 5 more rows
 ```
 
 Note that there is a `tidy()` method for `glmnet` objects in the `broom` package. If this is used directly on the underlying `glmnet` object, it returns _all of coefficients on the path_:
@@ -191,7 +191,7 @@ all_tidy_coefs
 ## 4 (Intercept)     4     24.7   3.89     0.347
 ## 5 (Intercept)     5     26.0   3.55     0.429
 ## 6 (Intercept)     6     27.2   3.23     0.497
-## # … with 634 more rows
+## # ℹ 634 more rows
 ```
 
 ```r