more documentation for #362

Author: topepo

man/boost_tree.Rd

@@ -38,6 +38,9 @@ mod_param <-
   update(sample_size = sample_prop(c(0.4, 0.9)))
 ```
 
+For this engine, tuning over `trees` is very efficient since the same model 
+object can be used to make predictions over multiple values of `trees`.  
+
 Finally, note that `xgboost` models require that non-numeric predictors (e.g., factors) must be converted to dummy variables or some other numeric representation. By default, when using `fit()` with `xgboost`, a one-hot encoding is used to convert factor predictors to indicator variables. 
 
 
@@ -52,6 +55,9 @@ boost_tree() %>%
 
 Note that [C50::C5.0()] does not require factor predictors to be converted  to indicator variables. `fit()` does not affect the encoding of the predictor values (i.e. factors stay factors) for this model.
 
+For this engine, tuning over `trees` is very efficient since the same model 
+object can be used to make predictions over multiple values of `trees`.  
+
 ## spark
 
 ```{r spark-reg}

man/mars.Rd

@@ -26,6 +26,8 @@ Note that, when the model is fit, the `earth` package only has its  namespace lo
 
 Also, `fit()` passes the data directly to `earth::earth()` so that its formula method can create dummy variables as-needed. 
 
+For this engine, tuning over `num_terms` is very efficient since the same model 
+object can be used to make predictions over multiple values of `num_terms`.  
 
 ## Parameter translations
 

man/nearest_neighbor.Rd

@@ -27,6 +27,9 @@ utilize the underlying `predict.train.kknn` method to predict on new data. This
 also means that a single value of that function's `kernel` argument (a.k.a
 `weight_func` here) can be supplied
 
+For this engine, tuning over `neighbors` is very efficient since the same model 
+object can be used to make predictions over multiple values of `neighbors`. 
+
 ## Parameter translations
 
 The standardized parameter names in parsnip can be mapped to their original