diff --git a/src/python/nimbusml.pyproj b/src/python/nimbusml.pyproj
index 1cf0f52f..e27fedd2 100644
--- a/src/python/nimbusml.pyproj
+++ b/src/python/nimbusml.pyproj
@@ -738,6 +738,7 @@
     <Compile Include="nimbusml\tests\preprocessing\test_tostring.py" />
     <Compile Include="nimbusml\tests\preprocessing\text\test_wordtokenizer.py" />
     <Compile Include="nimbusml\tests\test_csr_matrix_output.py" />
+    <Compile Include="nimbusml\tests\test_fit_graph.py" />
     <Compile Include="nimbusml\tests\test_variable_column.py" />
     <Compile Include="nimbusml\tests\timeseries\test_iidchangepointdetector.py" />
     <Compile Include="nimbusml\tests\timeseries\test_ssaforecaster.py" />
diff --git a/src/python/nimbusml/tests/pipeline/test_pipeline_split_models.py b/src/python/nimbusml/tests/pipeline/test_pipeline_split_models.py
index bc1399bf..4a6d9109 100644
--- a/src/python/nimbusml/tests/pipeline/test_pipeline_split_models.py
+++ b/src/python/nimbusml/tests/pipeline/test_pipeline_split_models.py
@@ -47,7 +47,7 @@ def test_notvectorized_output_predictor_model(self):
 
         # Create and fit a combined model and spit out predictor model
         combined_pipeline = Pipeline([RangeFilter(min=0.0, max=4.5) << 'c2',
-                            OnlineGradientDescentRegressor(label='c2')],
+                            OnlineGradientDescentRegressor(feature=['c1'], label='c2')],
                            random_state=seed)
         combined_pipeline.fit(df, output_predictor_model=True)
         result_1 = combined_pipeline.predict(df)