diff --git a/R-package/R/xgb.train.R b/R-package/R/xgb.train.R
index 20908863f..3fb42b6ab 100644
--- a/R-package/R/xgb.train.R
+++ b/R-package/R/xgb.train.R
@@ -42,8 +42,9 @@
 #'     \item \code{reg:logistic} logistic regression.
 #'     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
 #'     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
-#'     \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes).
-#'     \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probability of each data point belonging to each class.
+#'     \item \code{num_class} set the number of classes. To use only with multiclass objectives.
+#'     \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is a number and should be from 0 \code{tonum_class}
+#'     \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
 #'     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
 #'   }
 #'   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5
diff --git a/R-package/man/xgb.train.Rd b/R-package/man/xgb.train.Rd
index 3f93b3989..1bd243d60 100644
--- a/R-package/man/xgb.train.Rd
+++ b/R-package/man/xgb.train.Rd
@@ -1,4 +1,4 @@
-% Generated by roxygen2 (4.1.0): do not edit by hand
+% Generated by roxygen2 (4.1.1): do not edit by hand
 % Please edit documentation in R/xgb.train.R
 \name{xgb.train}
 \alias{xgb.train}
@@ -48,8 +48,9 @@ xgb.train(params = list(), data, nrounds, watchlist = list(), obj = NULL,
     \item \code{reg:logistic} logistic regression.
     \item \code{binary:logistic} logistic regression for binary classification. Output probability.
     \item \code{binary:logitraw} logistic regression for binary classification, output score before logistic transformation.
-    \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective, you also need to set num_class(number of classes).
-    \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probability of each data point belonging to each class.
+    \item \code{num_class} set the number of classes. To use only with multiclass objectives.
+    \item \code{multi:softmax} set xgboost to do multiclass classification using the softmax objective. Class is a number and should be from 0 \code{tonum_class}
+    \item \code{multi:softprob} same as softmax, but output a vector of ndata * nclass, which can be further reshaped to ndata, nclass matrix. The result contains predicted probabilities of each data point belonging to each class.
     \item \code{rank:pairwise} set xgboost to do ranking task by minimizing the pairwise loss.
   }
   \item \code{base_score} the initial prediction score of all instances, global bias. Default: 0.5