Minor changes to mode and engine docs

Author: juliasilge

R/boost_tree.R

@@ -29,11 +29,12 @@
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"xgboost"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"xgboost"`.
 #' @param mtry A number for the number (or proportion) of predictors that will
 #'  be randomly sampled at each split when creating the tree models (`xgboost`
 #'  only).

R/decision_tree.R

@@ -21,11 +21,12 @@
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"rpart"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"rpart"`.
 #' @param cost_complexity A positive number for the the cost/complexity
 #'   parameter (a.k.a. `Cp`) used by CART models (`rpart` only).
 #' @param tree_depth An integer for maximum depth of the tree.

R/linear_reg.R

@@ -16,10 +16,11 @@
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  The only possible value for this model is "regression".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"lm"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"lm"`.
 #' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization

R/logistic_reg.R

@@ -16,10 +16,11 @@
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  The only possible value for this model is "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"glm"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"glm"`.
 #' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `LiblineaR`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization

R/mars.R

@@ -22,11 +22,12 @@
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"earth"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"earth"`.
 #' @param num_terms The number of features that will be retained in the
 #'    final model, including the intercept.
 #' @param prod_degree The highest possible interaction degree.

R/mlp.R

@@ -27,11 +27,12 @@
 #'  If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"nnet"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"nnet"`.
 #' @param hidden_units An integer for the number of units in the hidden model.
 #' @param penalty A non-negative numeric value for the amount of weight
 #'  decay.

R/multinom_reg.R

@@ -16,10 +16,11 @@
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  The only possible value for this model is "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"nnet"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"nnet"`.
 #' @param penalty A non-negative number representing the total
 #'  amount of regularization (`glmnet`, `keras`, and `spark` only).
 #'  For `keras` models, this corresponds to purely L2 regularization
@@ -35,7 +36,7 @@
 #' The model can be created using the `fit()` function using the
 #'  following _engines_:
 #' \itemize{
-#' \item \pkg{R}:   `"glmnet"`, `"nnet"` (the default)
+#' \item \pkg{R}:   `"nnet"` (the default), `"glmnet"`
 #' \item \pkg{Spark}: `"spark"`
 #' \item \pkg{keras}: `"keras"`
 #' }

R/nearest_neighbor.R

@@ -23,11 +23,12 @@
 #'  here (`NULL`), the values are taken from the underlying model
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #' Possible values for this model are `"unknown"`, `"regression"`, or
 #' `"classification"`.
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"kknn"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"kknn"`.
 #' @param neighbors A single integer for the number of neighbors
 #' to consider (often called `k`). For \pkg{kknn}, a value of 5
 #' is used if `neighbors` is not specified.

R/proportional_hazards.R

@@ -16,10 +16,11 @@
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", or "censored regression".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"survival"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"survival"`.
 #' @inheritParams linear_reg
 #'
 #' @details

R/rand_forest.R

@@ -20,11 +20,12 @@
 #'  functions. If parameters need to be modified, `update()` can be used
 #'  in lieu of recreating the object from scratch.
 #'
-#' @param mode A single character string for the type of model.
+#' @param mode A single character string for the prediction outcome mode.
 #'  Possible values for this model are "unknown", "regression", or
 #'  "classification".
-#' @param engine A character string for the method of fitting. Possible engines
-#' are listed above. The default for this model is `"ranger"`.
+#' @param engine A single character string specifying what computational engine
+#'  to use for fitting. Possible engines are listed below. The default for this
+#'  model is `"ranger"`.
 #' @param mtry An integer for the number of predictors that will
 #'  be randomly sampled at each split when creating the tree models.
 #' @param trees An integer for the number of trees contained in