scikit-learn
diff --git a/‎sklearn/inspection/tests/test_partial_dependence.py
Copy file name to clipboardExpand all lines: sklearn/inspection/tests/test_partial_dependence.py
+2-1Lines changed: 2 additions & 1 deletion b/‎sklearn/inspection/tests/test_partial_dependence.py
Copy file name to clipboardExpand all lines: sklearn/inspection/tests/test_partial_dependence.py
+2-1Lines changed: 2 additions & 1 deletion
diff --git a/‎sklearn/linear_model/tests/test_logistic.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/tests/test_logistic.py
+5-9Lines changed: 5 additions & 9 deletions b/‎sklearn/linear_model/tests/test_logistic.py
Copy file name to clipboardExpand all lines: sklearn/linear_model/tests/test_logistic.py
+5-9Lines changed: 5 additions & 9 deletions
@@ -30,6 +30,7 @@
 from sklearn.preprocessing import PolynomialFeatures
 from sklearn.preprocessing import StandardScaler
 from sklearn.preprocessing import RobustScaler
+from sklearn.preprocessing import scale
 from sklearn.pipeline import make_pipeline
 from sklearn.dummy import DummyClassifier
 from sklearn.base import BaseEstimator, ClassifierMixin, clone
@@ -607,7 +608,7 @@ def test_partial_dependence_dataframe(estimator, preprocessor, features):
     # check that the partial dependence support dataframe and pipeline
     # including a column transformer
     pd = pytest.importorskip("pandas")
-    df = pd.DataFrame(iris.data, columns=iris.feature_names)
+    df = pd.DataFrame(scale(iris.data), columns=iris.feature_names)
 
     pipe = make_pipeline(preprocessor, estimator)
     pipe.fit(df, iris.target)
 
@@ -1,5 +1,4 @@
 import os
-import sys
 import warnings
 import numpy as np
 from numpy.testing import assert_allclose, assert_almost_equal
@@ -1691,9 +1690,9 @@ def test_logistic_regression_path_coefs_multinomial():
 
 
 @pytest.mark.parametrize('est',
-                         [LogisticRegression(random_state=0),
+                         [LogisticRegression(random_state=0, max_iter=500),
                           LogisticRegressionCV(random_state=0, cv=3,
-                                               Cs=3, tol=1e-3)],
+                                               Cs=3, tol=1e-3, max_iter=500)],
                          ids=lambda x: x.__class__.__name__)
 @pytest.mark.parametrize('solver', ['liblinear', 'lbfgs', 'newton-cg', 'sag',
                                     'saga'])
@@ -1703,8 +1702,9 @@ def test_logistic_regression_multi_class_auto(est, solver):
     def fit(X, y, **kw):
         return clone(est).set_params(**kw).fit(X, y)
 
-    X = iris.data[::10]
-    X2 = iris.data[1::10]
+    scaled_data = scale(iris.data)
+    X = scaled_data[::10]
+    X2 = scaled_data[1::10]
     y_multi = iris.target[::10]
     y_bin = y_multi == 0
     est_auto_bin = fit(X, y_bin, multi_class='auto', solver=solver)
@@ -1722,10 +1722,6 @@ def fit(X, y, **kw):
     else:
         est_multi_multi = fit(X, y_multi, multi_class='multinomial',
                               solver=solver)
-        if sys.platform == 'darwin' and solver == 'lbfgs':
-            pytest.xfail('Issue #11924: LogisticRegressionCV(solver="lbfgs", '
-                         'multi_class="multinomial") is nondeterministic on '
-                         'MacOS.')
         assert_allclose(est_auto_multi.coef_, est_multi_multi.coef_)
         assert_allclose(est_auto_multi.predict_proba(X2),
                         est_multi_multi.predict_proba(X2))