diff --git a/flaml/version.py b/flaml/version.py
index 61fb31cae..1f4c4d43b 100644
--- a/flaml/version.py
+++ b/flaml/version.py
@@ -1 +1 @@
-__version__ = "0.10.0"
+__version__ = "0.10.1"
diff --git a/website/docs/Use-Cases/Task-Oriented-AutoML.md b/website/docs/Use-Cases/Task-Oriented-AutoML.md
index e5b2154ba..b045a690f 100644
--- a/website/docs/Use-Cases/Task-Oriented-AutoML.md
+++ b/website/docs/Use-Cases/Task-Oriented-AutoML.md
@@ -301,7 +301,8 @@ By default, flaml uses the following method to split the data:
 * time-based split for time series forecasting;
 * group-based split for learning to rank.
 
-The data split method for classification can be changed into uniform split by setting `split_type="uniform"`. For both classification and regression, time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`.
+The data split method for classification can be changed into uniform split by setting `split_type="uniform"`. The data are shuffled when `split_type in ("uniform", "stratified")`.
+For both classification and regression, time-based split can be enforced if the data are sorted by timestamps, by setting `split_type="time"`.
 
 When `eval_method="cv"`, `split_type` can also be set as a custom splitter. It needs to be an instance of a derived class of scikit-learn
 [KFold](https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html#sklearn.model_selection.KFold)
diff --git a/website/docs/Use-Cases/Zero-Shot-AutoML.md b/website/docs/Use-Cases/Zero-Shot-AutoML.md
index 1f3a6239a..c8871a3fd 100644
--- a/website/docs/Use-Cases/Zero-Shot-AutoML.md
+++ b/website/docs/Use-Cases/Zero-Shot-AutoML.md
@@ -19,7 +19,7 @@ from lightgbm import LGBMRegressor
 
 estimator = LGBMRegressor()
 estimator.fit(X_train, y_train)
-estimator.predict(X_test, y_test)
+estimator.predict(X_test)
 ```
 
 Simply replace the first line with: