scikit-learn
diff --git a/‎doc/whats_new/v1.0.rst
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diff --git a/‎sklearn/pipeline.py
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+14-4Lines changed: 14 additions & 4 deletions b/‎sklearn/pipeline.py
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+14-4Lines changed: 14 additions & 4 deletions
diff --git a/‎sklearn/tests/test_pipeline.py
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+16-4Lines changed: 16 additions & 4 deletions b/‎sklearn/tests/test_pipeline.py
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+16-4Lines changed: 16 additions & 4 deletions
@@ -266,6 +266,14 @@ Changelog
   Use ``var_`` instead.
   :pr:`18842` by :user:`Hong Shao Yang <hongshaoyang>`.
 
+:mod:`sklearn.pipeline`
+.......................
+
+- |API| The `predict_proba` and `predict_log_proba` methods of the
+  :class:`Pipeline` class now support passing prediction kwargs to
+  the final estimator.
+  :pr:`19790` by :user:`Christopher Flynn <crflynn>`.
+
 :mod:`sklearn.preprocessing`
 ............................
 
 
@@ -456,7 +456,7 @@ def fit_predict(self, X, y=None, **fit_params):
         return y_pred
 
     @if_delegate_has_method(delegate='_final_estimator')
-    def predict_proba(self, X):
+    def predict_proba(self, X, **predict_proba_params):
         """Apply transforms, and predict_proba of the final estimator
 
         Parameters
@@ -465,14 +465,18 @@ def predict_proba(self, X):
             Data to predict on. Must fulfill input requirements of first step
             of the pipeline.
 
+        **predict_proba_params : dict of string -> object
+            Parameters to the ``predict_proba`` called at the end of all
+            transformations in the pipeline.
+
         Returns
         -------
         y_proba : array-like of shape (n_samples, n_classes)
         """
         Xt = X
         for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
-        return self.steps[-1][-1].predict_proba(Xt)
+        return self.steps[-1][-1].predict_proba(Xt, **predict_proba_params)
 
     @if_delegate_has_method(delegate='_final_estimator')
     def decision_function(self, X):
@@ -513,7 +517,7 @@ def score_samples(self, X):
         return self.steps[-1][-1].score_samples(Xt)
 
     @if_delegate_has_method(delegate='_final_estimator')
-    def predict_log_proba(self, X):
+    def predict_log_proba(self, X, **predict_log_proba_params):
         """Apply transforms, and predict_log_proba of the final estimator
 
         Parameters
@@ -522,14 +526,20 @@ def predict_log_proba(self, X):
             Data to predict on. Must fulfill input requirements of first step
             of the pipeline.
 
+        **predict_log_proba_params : dict of string -> object
+            Parameters to the ``predict_log_proba`` called at the end of all
+            transformations in the pipeline.
+
         Returns
         -------
         y_score : array-like of shape (n_samples, n_classes)
         """
         Xt = X
         for _, name, transform in self._iter(with_final=False):
             Xt = transform.transform(Xt)
-        return self.steps[-1][-1].predict_log_proba(Xt)
+        return self.steps[-1][-1].predict_log_proba(
+            Xt, **predict_log_proba_params
+        )
 
     @property
     def transform(self):
 
@@ -159,6 +159,14 @@ def predict(self, X, got_attribute=False):
         self.got_attribute = got_attribute
         return self
 
+    def predict_proba(self, X, got_attribute=False):
+        self.got_attribute = got_attribute
+        return self
+
+    def predict_log_proba(self, X, got_attribute=False):
+        self.got_attribute = got_attribute
+        return self
+
 
 def test_pipeline_init():
     # Test the various init parameters of the pipeline.
@@ -448,12 +456,16 @@ def test_fit_predict_with_intermediate_fit_params():
     assert 'should_succeed' not in pipe.named_steps['transf'].fit_params
 
 
-def test_predict_with_predict_params():
-    # tests that Pipeline passes predict_params to the final estimator
-    # when predict is invoked
+@pytest.mark.parametrize("method_name", [
+    "predict", "predict_proba", "predict_log_proba"
+])
+def test_predict_methods_with_predict_params(method_name):
+    # tests that Pipeline passes predict_* to the final estimator
+    # when predict_* is invoked
     pipe = Pipeline([('transf', Transf()), ('clf', DummyEstimatorParams())])
     pipe.fit(None, None)
-    pipe.predict(X=None, got_attribute=True)
+    method = getattr(pipe, method_name)
+    method(X=None, got_attribute=True)
 
     assert pipe.named_steps['clf'].got_attribute