diff --git a/examples/distributed/graphsage/node_classification_unsupervised.py b/examples/distributed/graphsage/node_classification_unsupervised.py
index 4b71f599064b..2b030646bf28 100644
--- a/examples/distributed/graphsage/node_classification_unsupervised.py
+++ b/examples/distributed/graphsage/node_classification_unsupervised.py
@@ -175,7 +175,7 @@ def compute_acc(emb, labels, train_nids, val_nids, test_nids):
     labels = labels.cpu().numpy()
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], labels[train_nids])
 
     pred = lr.predict(emb)
diff --git a/examples/pytorch/graphsage/advanced/model.py b/examples/pytorch/graphsage/advanced/model.py
index 7dede9643cc4..edb965b78936 100644
--- a/examples/pytorch/graphsage/advanced/model.py
+++ b/examples/pytorch/graphsage/advanced/model.py
@@ -104,7 +104,7 @@ def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids):
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
 
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], train_labels)
 
     pred = lr.predict(emb)
diff --git a/examples/pytorch/graphsage/dist/train_dist_unsupervised.py b/examples/pytorch/graphsage/dist/train_dist_unsupervised.py
index f3eb1a24305b..24d737f4f280 100644
--- a/examples/pytorch/graphsage/dist/train_dist_unsupervised.py
+++ b/examples/pytorch/graphsage/dist/train_dist_unsupervised.py
@@ -175,7 +175,7 @@ def compute_acc(emb, labels, train_nids, val_nids, test_nids):
     labels = labels.cpu().numpy()
 
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], labels[train_nids])
 
     pred = lr.predict(emb)
diff --git a/examples/pytorch/metapath2vec/test.py b/examples/pytorch/metapath2vec/test.py
index 7f293daa1b7f..ca6ab0ae268f 100644
--- a/examples/pytorch/metapath2vec/test.py
+++ b/examples/pytorch/metapath2vec/test.py
@@ -94,13 +94,13 @@
         author_true = np.array(author_true)
         file.close()
         print("beging predicting")
-        clf_venue = LogisticRegression(
-            random_state=0, solver="lbfgs", multi_class="multinomial"
-        ).fit(venue_training, venue_label)
+        clf_venue = LogisticRegression(random_state=0, solver="lbfgs").fit(
+            venue_training, venue_label
+        )
         y_pred_venue = clf_venue.predict(venue_testing)
-        clf_author = LogisticRegression(
-            random_state=0, solver="lbfgs", multi_class="multinomial"
-        ).fit(author_training, author_label)
+        clf_author = LogisticRegression(random_state=0, solver="lbfgs").fit(
+            author_training, author_label
+        )
         y_pred_author = clf_author.predict(author_testing)
         macro_average_venue += f1_score(
             venue_true, y_pred_venue, average="macro"
diff --git a/examples/pytorch/multigpu/multi_gpu_link_prediction.py b/examples/pytorch/multigpu/multi_gpu_link_prediction.py
index bb0c64f75c4f..52b5204e4c8e 100644
--- a/examples/pytorch/multigpu/multi_gpu_link_prediction.py
+++ b/examples/pytorch/multigpu/multi_gpu_link_prediction.py
@@ -142,7 +142,7 @@ def compute_acc_unsupervised(emb, labels, train_nids, val_nids, test_nids):
     test_nids = test_nids.cpu().numpy()
     test_labels = labels[test_nids]
     emb = (emb - emb.mean(0, keepdims=True)) / emb.std(0, keepdims=True)
-    lr = lm.LogisticRegression(multi_class="multinomial", max_iter=10000)
+    lr = lm.LogisticRegression(max_iter=10000)
     lr.fit(emb[train_nids], train_labels)
     pred = lr.predict(emb)
     f1_micro_eval = skm.f1_score(val_labels, pred[val_nids], average="micro")
diff --git a/examples/pytorch/node2vec/model.py b/examples/pytorch/node2vec/model.py
index 3b2db93801c0..282799bbe1fb 100644
--- a/examples/pytorch/node2vec/model.py
+++ b/examples/pytorch/node2vec/model.py
@@ -190,9 +190,9 @@ def evaluate(self, x_train, y_train, x_val, y_val):
 
         x_train, y_train = x_train.cpu().numpy(), y_train.cpu().numpy()
         x_val, y_val = x_val.cpu().numpy(), y_val.cpu().numpy()
-        lr = LogisticRegression(
-            solver="lbfgs", multi_class="auto", max_iter=150
-        ).fit(x_train, y_train)
+        lr = LogisticRegression(solver="lbfgs", max_iter=150).fit(
+            x_train, y_train
+        )
 
         return lr.score(x_val, y_val)