scikit-learn
diff --git a/‎sklearn/cross_decomposition/_pls.py
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+3-3Lines changed: 3 additions & 3 deletions b/‎sklearn/cross_decomposition/_pls.py
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+3-3Lines changed: 3 additions & 3 deletions
diff --git a/‎sklearn/tests/test_common.py
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-1Lines changed: 0 additions & 1 deletion b/‎sklearn/tests/test_common.py
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-1Lines changed: 0 additions & 1 deletion
diff --git a/‎sklearn/utils/estimator_checks.py
Copy file name to clipboardExpand all lines: sklearn/utils/estimator_checks.py
+8-3Lines changed: 8 additions & 3 deletions b/‎sklearn/utils/estimator_checks.py
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+8-3Lines changed: 8 additions & 3 deletions
@@ -317,7 +317,7 @@ def transform(self, X, Y=None, copy=True):
         `x_scores` if `Y` is not given, `(x_scores, y_scores)` otherwise.
         """
         check_is_fitted(self)
-        X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
+        X = self._validate_data(X, copy=copy, dtype=FLOAT_DTYPES, reset=False)
         # Normalize
         X -= self._x_mean
         X /= self._x_std
@@ -379,7 +379,7 @@ def predict(self, X, copy=True):
         space.
         """
         check_is_fitted(self)
-        X = check_array(X, copy=copy, dtype=FLOAT_DTYPES)
+        X = self._validate_data(X, copy=copy, dtype=FLOAT_DTYPES, reset=False)
         # Normalize
         X -= self._x_mean
         X /= self._x_std
@@ -984,7 +984,7 @@ def transform(self, X, Y=None):
             `(X_transformed, Y_transformed)` otherwise.
         """
         check_is_fitted(self)
-        X = check_array(X, dtype=np.float64)
+        X = self._validate_data(X, dtype=np.float64, reset=False)
         Xr = (X - self._x_mean) / self._x_std
         x_scores = np.dot(Xr, self.x_weights_)
         if Y is not None:
 
@@ -267,7 +267,6 @@ def test_search_cv(estimator, check, request):
     'calibration',
     'compose',
     'covariance',
-    'cross_decomposition',
     'discriminant_analysis',
     'ensemble',
     'feature_extraction',
 
@@ -63,7 +63,7 @@
     load_iris,
     make_blobs,
     make_multilabel_classification,
-    make_regression,
+    make_regression
 )
 
 REGRESSION_DATASET = None
@@ -646,6 +646,9 @@ def _set_checking_parameters(estimator):
     if name == 'OneHotEncoder':
         estimator.set_params(handle_unknown='ignore')
 
+    if name in CROSS_DECOMPOSITION:
+        estimator.set_params(n_components=1)
+
 
 class _NotAnArray:
     """An object that is convertible to an array.
@@ -3122,9 +3125,11 @@ def check_n_features_in_after_fitting(name, estimator_orig):
     if 'warm_start' in estimator.get_params():
         estimator.set_params(warm_start=False)
 
-    n_samples = 100
-    X = rng.normal(loc=100, size=(n_samples, 2))
+    n_samples = 150
+    X = rng.normal(size=(n_samples, 8))
+    X = _enforce_estimator_tags_x(estimator, X)
     X = _pairwise_estimator_convert_X(X, estimator)
+
     if is_regressor(estimator):
         y = rng.normal(size=n_samples)
     else: