@@ -532,9 +532,7 @@ def sklearn_classification_model(iris_dataset):
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = sk.LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=1000
-        )
+        model = sk.LogisticRegression(solver="lbfgs", max_iter=1000)
         model.fit(X, y)
     return model
 
 
@@ -42,7 +42,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(multi_class="multinomial", solver="lbfgs")
+        model = LogisticRegression(solver="lbfgs")
         model.fit(X, y)
     return model
 
 
@@ -63,9 +63,7 @@ def sklearn_model(train_data):
     X, y = train_data
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=10000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=10000)
         model.fit(X, y)
     return model
 
 
@@ -40,9 +40,7 @@ def sklearn_logistic_model():
 
     with warnings.catch_warnings():
         warnings.simplefilter("ignore")
-        model = LogisticRegression(
-            multi_class="multinomial", solver="lbfgs", max_iter=10000
-        )
+        model = LogisticRegression(solver="lbfgs", max_iter=10000)
         model.fit(iris.iloc[:, 0:4], iris["Species"])
 
     return model, iris.iloc[:, 0:4]