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
diff --git a/R/mlp.R b/R/mlp.R
index b71ac9453..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:
@@ -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