From 7e0ec69c5554bda4ca64f7d98f8f67d6d1d92c9f Mon Sep 17 00:00:00 2001 From: Rory Nolan Date: Thu, 21 May 2020 18:53:59 -0700 Subject: [PATCH 1/3] Typo correction. --- R/decision_tree.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/decision_tree.R b/R/decision_tree.R index 9ce767dad..4bb0c7271 100644 --- a/R/decision_tree.R +++ b/R/decision_tree.R @@ -102,7 +102,7 @@ print.decision_tree <- function(x, ...) { #' @export #' @inheritParams update.boost_tree -#' @param object A random forest model specification. +#' @param object A decision tree model specification. #' @examples #' model <- decision_tree(cost_complexity = 10, min_n = 3) #' model From beb1c5eeb55dd0550d454641aede9b5f430906a5 Mon Sep 17 00:00:00 2001 From: Rory Nolan Date: Thu, 21 May 2020 18:57:20 -0700 Subject: [PATCH 2/3] Correct typo. --- R/mlp.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/mlp.R b/R/mlp.R index b71ac9453..74f936bf6 100644 --- a/R/mlp.R +++ b/R/mlp.R @@ -105,7 +105,7 @@ print.mlp <- function(x, ...) { #' #' @export #' @inheritParams update.boost_tree -#' @param object A random forest model specification. +#' @param object A multilayer perceptron model specification. #' @examples #' model <- mlp(hidden_units = 10, dropout = 0.30) #' model From 65c1faea9e31edf642102acaf43eacbdeacff1ff Mon Sep 17 00:00:00 2001 From: Rory Nolan Date: Thu, 21 May 2020 19:07:11 -0700 Subject: [PATCH 3/3] Tiny typo. --- R/mlp.R | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/R/mlp.R b/R/mlp.R index 74f936bf6..2ca3565ac 100644 --- a/R/mlp.R +++ b/R/mlp.R @@ -37,7 +37,7 @@ #' @param dropout A number between 0 (inclusive) and 1 denoting the proportion #' of model parameters randomly set to zero during model training. #' @param epochs An integer for the number of training iterations. -#' @param activation A single character strong denoting the type of relationship +#' @param activation A single character string denoting the type of relationship #' between the original predictors and the hidden unit layer. The activation #' function between the hidden and output layers is automatically set to either #' "linear" or "softmax" depending on the type of outcome. Possible values are: