scikit-learn
diff --git a/‎sklearn/compose/_target.py
Lines changed: 14 additions & 7 deletions b/‎sklearn/compose/_target.py
Lines changed: 14 additions & 7 deletions
diff --git a/‎sklearn/compose/tests/test_target.py
Lines changed: 28 additions & 0 deletions b/‎sklearn/compose/tests/test_target.py
Lines changed: 28 additions & 0 deletions
@@ -113,6 +113,12 @@ def __init__(self, regressor=None, transformer=None,
         self.check_inverse = check_inverse
 
     def _fit_transformer(self, y):
+        """Check transformer and fit transformer.
+
+        Create the default transformer, fit it and make additional inverse
+        check on a subset (optional).
+
+        """
         if (self.transformer is not None and
                 (self.func is not None or self.inverse_func is not None)):
             raise ValueError("'transformer' and functions 'func'/"
@@ -177,19 +183,20 @@ def fit(self, X, y, sample_weight=None):
             y_2d = y
         self._fit_transformer(y_2d)
 
-        if self.regressor is None:
-            from ..linear_model import LinearRegression
-            self.regressor_ = LinearRegression()
-        else:
-            self.regressor_ = clone(self.regressor)
-
         # transform y and convert back to 1d array if needed
-        y_trans = self.transformer_.fit_transform(y_2d)
+        y_trans = self.transformer_.transform(y_2d)
         # FIXME: a FunctionTransformer can return a 1D array even when validate
         # is set to True. Therefore, we need to check the number of dimension
         # first.
         if y_trans.ndim == 2 and y_trans.shape[1] == 1:
             y_trans = y_trans.squeeze(axis=1)
+
+        if self.regressor is None:
+            from ..linear_model import LinearRegression
+            self.regressor_ = LinearRegression()
+        else:
+            self.regressor_ = clone(self.regressor)
+
         if sample_weight is None:
             self.regressor_.fit(X, y_trans)
         else:
 
@@ -265,3 +265,31 @@ def test_transform_target_regressor_ensure_y_array():
     tt.predict(X.tolist())
     assert_raises(AssertionError, tt.fit, X, y.tolist())
     assert_raises(AssertionError, tt.predict, X)
+
+
+class DummyTransformer(BaseEstimator, TransformerMixin):
+    """Dummy transformer which count how many time fit was called."""
+    def __init__(self, fit_counter=0):
+        self.fit_counter = fit_counter
+
+    def fit(self, X, y=None):
+        self.fit_counter += 1
+        return self
+
+    def transform(self, X):
+        return X
+
+    def inverse_transform(self, X):
+        return X
+
+
+@pytest.mark.parametrize("check_inverse", [False, True])
+def test_transform_target_regressor_count_fit(check_inverse):
+    # regression test for gh-issue #11618
+    # check that we only call a single time fit for the transformer
+    X, y = friedman
+    ttr = TransformedTargetRegressor(
+        transformer=DummyTransformer(), check_inverse=check_inverse
+    )
+    ttr.fit(X, y)
+    assert ttr.transformer_.fit_counter == 1