remove bundle reference for lightgbm (#1133)

man/rmd/boost_tree_lightgbm.Rmd

@@ -74,11 +74,6 @@ Non-numeric predictors (i.e., factors) are internally converted to numeric. In t
 ```{r child = "template-mtry-prop.Rmd"}
 ```
 
-### Saving fitted model objects
-
-```{r child = "template-bundle.Rmd"}
-```
-
 ### Bagging
 
 The `sample_size` argument is translated to the `bagging_fraction` parameter in the `param` argument of `lgb.train`. The argument is interpreted by lightgbm as a _proportion_ rather than a count, so bonsai internally reparameterizes the `sample_size` argument with [dials::sample_prop()] during tuning. 

man/rmd/boost_tree_lightgbm.md

@@ -32,7 +32,7 @@ Note that parsnip's translation can be overridden via the `counts` argument, sup
 The **bonsai** extension package is required to fit this model.
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), tree_depth = integer(), 
   learn_rate = numeric(), min_n = integer(), loss_reduction = numeric()
@@ -57,18 +57,19 @@ boost_tree(
 ## 
 ## Model fit template:
 ## bonsai::train_lightgbm(x = missing_arg(), y = missing_arg(), 
-##     feature_fraction_bynode = integer(), num_iterations = integer(), 
-##     min_data_in_leaf = integer(), max_depth = integer(), learning_rate = numeric(), 
-##     min_gain_to_split = numeric(), verbose = -1, num_threads = 0, 
-##     seed = sample.int(10^5, 1), deterministic = TRUE)
+##     weights = missing_arg(), feature_fraction_bynode = integer(), 
+##     num_iterations = integer(), min_data_in_leaf = integer(), 
+##     max_depth = integer(), learning_rate = numeric(), min_gain_to_split = numeric(), 
+##     verbose = -1, num_threads = 0, seed = sample.int(10^5, 1), 
+##     deterministic = TRUE)
 ```
 
 ## Translation from parsnip to the original package (classification)
 
 The **bonsai** extension package is required to fit this model.
 
 
-```r
+``` r
 boost_tree(
   mtry = integer(), trees = integer(), tree_depth = integer(), 
   learn_rate = numeric(), min_n = integer(), loss_reduction = numeric()
@@ -93,10 +94,11 @@ boost_tree(
 ## 
 ## Model fit template:
 ## bonsai::train_lightgbm(x = missing_arg(), y = missing_arg(), 
-##     feature_fraction_bynode = integer(), num_iterations = integer(), 
-##     min_data_in_leaf = integer(), max_depth = integer(), learning_rate = numeric(), 
-##     min_gain_to_split = numeric(), verbose = -1, num_threads = 0, 
-##     seed = sample.int(10^5, 1), deterministic = TRUE)
+##     weights = missing_arg(), feature_fraction_bynode = integer(), 
+##     num_iterations = integer(), min_data_in_leaf = integer(), 
+##     max_depth = integer(), learning_rate = numeric(), min_gain_to_split = numeric(), 
+##     verbose = -1, num_threads = 0, seed = sample.int(10^5, 1), 
+##     deterministic = TRUE)
 ```
 
 [bonsai::train_lightgbm()] is a wrapper around [lightgbm::lgb.train()] (and other functions) that make it easier to run this model. 
@@ -121,11 +123,6 @@ parsnip and its extensions accommodate this parameterization using the `counts`
 
 `mtry` is a main model argument for \\code{\\link[=boost_tree]{boost_tree()}} and \\code{\\link[=rand_forest]{rand_forest()}}, and thus should not have an engine-specific interface. So, regardless of engine, `counts` defaults to `TRUE`. For engines that support the proportion interpretation (currently `"xgboost"` and `"xrf"`, via the rules package, and `"lightgbm"` via the bonsai package) the user can pass the `counts = FALSE` argument to `set_engine()` to supply `mtry` values within `[0, 1]`.
 
-### Saving fitted model objects
-
-
-Models fitted with this engine may require native serialization methods to be properly saved and/or passed between R sessions. To learn more about preparing fitted models for serialization, see the bundle package.
-
 ### Bagging
 
 The `sample_size` argument is translated to the `bagging_fraction` parameter in the `param` argument of `lgb.train`. The argument is interpreted by lightgbm as a _proportion_ rather than a count, so bonsai internally reparameterizes the `sample_size` argument with [dials::sample_prop()] during tuning.