diff --git a/R/machine_learning.R b/R/machine_learning.R
index 92159d60a06e097a963344441d59ed55065ffb35..f57df72e3637f1b72db97ea0777f37f2272f4d1f 100644
--- a/R/machine_learning.R
+++ b/R/machine_learning.R
@@ -5,7 +5,7 @@
 #' @param df_features <`data.frame`> contains the dataset ready to go through the models
 #' @param df_outcome <`data.frame`> contains the outcome variables: a single outcome variable in case of classification, and two outcome variables in case of survival analysis. Namely, in case of `main_task == "classification"`, `df_outcome` should contain columns `outcome_col` and `id_col`. In case of `main_task == "survival"`, `df_outcome` should contain columns `outcome_col`, "event_col" and `id_col`.
 #' @param job <`character`> can either be "train" or "predict"
-#' @param ml_model <`character`> model that should be used. Can either be "cox", "logistic", "random_forest", "random_survival_forest", "xgb_cox" or "xgb_aft"
+#' @param ml_model <`character`> model that should be used. Can either be "cox", "xgboost", "logistic", "random_forest", "random_survival_forest", "xgb_cox" or "xgb_aft"
 #' @param ml_options <`list`> specifying some needed arguments and options for machine learning. The list should contain the following elements:
 #' - t_max_survival: <`int`> maximum time of survival prediction, in case of `main_task == "survival"`
 #' @param feature_engineer <`character`> feature engineering method to be applied during transform. Either "umap", "pca" or "none"
@@ -92,7 +92,7 @@ machine_learning <- function(df_features,
     cat("Transforming the dataset...\n")
   }
   if (job == "train") {
-    if (dummy_binary){
+    if (dummy_binary) {
       transformer_results = transform_for_modeling_dichotomy(
         df               = df_features,
         action           = "train",
@@ -117,7 +117,7 @@ machine_learning <- function(df_features,
     )
   }
   if (job == "predict") {
-    if (dummy_binary){
+    if (dummy_binary) {
       df_features = purrr::pluck(
         transform_for_modeling_dichotomy(
           df     = df_features,
diff --git a/man/machine_learning.Rd b/man/machine_learning.Rd
index c32d62893829bc9f8451cdb86c3a2ee24d098bb8..e428509e82d97da5e9d4603e91b82b0862800215 100644
--- a/man/machine_learning.Rd
+++ b/man/machine_learning.Rd
@@ -29,7 +29,7 @@ machine_learning(
 
 \item{job}{<\code{character}> can either be "train" or "predict"}
 
-\item{ml_model}{<\code{character}> model that should be used. Can either be "cox", "logistic", "random_forest", "random_survival_forest", "xgb_cox" or "xgb_aft"}
+\item{ml_model}{<\code{character}> model that should be used. Can either be "cox", "xgboost", "logistic", "random_forest", "random_survival_forest", "xgb_cox" or "xgb_aft"}
 
 \item{outcome_col}{<\code{character}> name of the time column in case of \code{main_task == "survival"} or name of the single outcome column in case of \code{main_task == "classification"}}