diff --git a/python/cuml/cuml/internals/outputs.py b/python/cuml/cuml/internals/outputs.py
index 7d8df836e5..6e6c1a7c53 100644
--- a/python/cuml/cuml/internals/outputs.py
+++ b/python/cuml/cuml/internals/outputs.py
@@ -357,9 +357,11 @@ def reflect(
         provide ``None`` to disable this inference entirely; in this case the
         output type is expected to be specified manually either internal or
         external to the method.
-    reset : bool, default=False
-        Set to True for methods like ``fit`` that reset the reflected type on
-        an estimator.
+    reset : bool or "type", default=False
+        If True, both the features and reflected type are reset on the estimator.
+        If ``"type"``, only the reflected type is reset on the estimator.
+        Defaults to False, to not reset anything. Most estimators should set
+        ``reset=True`` on any fit-like methods.
     """
     # Local to avoid circular imports
     import cuml.accel
@@ -391,9 +393,12 @@ def reflect(
     if array is not None:
         array = _get_param(sig, array)
 
-    if reset and (model is None or array is None):
+    if reset not in (True, False, "type"):
+        raise ValueError(f"reset={reset!r} is not supported")
+
+    if (reset is not False) and (model is None or array is None):
         raise ValueError(
-            "`reset=True` is not valid with `array=None` or `model=None`"
+            f"`reset={reset}` is not valid with `array=None` or `model=None`"
         )
 
     @functools.wraps(func)
@@ -411,8 +416,9 @@ def inner(*args, **kwargs):
             array_arg = np.asarray(array_arg)
 
         with enter_internal_context() as was_external:
-            if reset:
+            if reset is not False:
                 model_arg._set_output_type(array_arg)
+            if reset is True:
                 check_features(model_arg, array_arg, reset=True)
 
             res = func(*args, **kwargs)
diff --git a/python/cuml/cuml/internals/validation.py b/python/cuml/cuml/internals/validation.py
index 1197172e2e..4fa0125ffb 100644
--- a/python/cuml/cuml/internals/validation.py
+++ b/python/cuml/cuml/internals/validation.py
@@ -71,7 +71,6 @@ def _get_n_features(X):
                 return len(row)
             except Exception:
                 pass
-        return 1
 
     if hasattr(X, "shape"):
         shape = X.shape
@@ -82,9 +81,38 @@ def _get_n_features(X):
     else:
         shape = np.asarray(X).shape
 
-    # TODO: Can remove the fallback to 1 when we finish dropping support
-    # for 1D X inputs
-    return shape[1] if len(shape) >= 2 else 1
+    ndim = len(shape)
+
+    if ndim != 2:
+        import cuml.accel
+
+        if isinstance(X, (cudf.Series, pd.Series)):
+            msg = (
+                f"Expected a 2-dimensional container but got {type(X).__name__} "
+                "instead. Pass a DataFrame containing a single row (i.e. "
+                "single sample) or a single column (i.e. single feature) "
+                "instead."
+            )
+        else:
+            kind = "scalar" if ndim == 0 else f"{ndim}D"
+            msg = (
+                f"Expected 2D array, got {kind} array instead. Reshape your data "
+                "using array.reshape(-1, 1) if your data has a single feature, "
+                "or array.reshape(1, -1) if it contains a single sample."
+            )
+
+        if cuml.accel.enabled() or ndim > 2:
+            raise ValueError(msg)
+        else:
+            warnings.warn(
+                "Support for passing non-2-dimensional X was deprecated in 26.04 "
+                "and will be removed in 26.06. In cuml 26.06 this will error "
+                f"with the following message:\n\n{msg}",
+                FutureWarning,
+            )
+            # Fallback to 1 feature until the deprecation is completed
+            return 1
+    return shape[1]
 
 
 def _warn_or_error(exc_cls, msg):
diff --git a/python/cuml/cuml/preprocessing/TargetEncoder.py b/python/cuml/cuml/preprocessing/TargetEncoder.py
index a0824a7243..5b2af8457f 100644
--- a/python/cuml/cuml/preprocessing/TargetEncoder.py
+++ b/python/cuml/cuml/preprocessing/TargetEncoder.py
@@ -174,8 +174,8 @@ class TargetEncoder(Base, InteropMixin):
     >>> test = DataFrame({'category': ['a', 'c', 'b', 'a']})
 
     >>> encoder = TargetEncoder(output_type='numpy')
-    >>> train_encoded = encoder.fit_transform(train.category, train.label)
-    >>> test_encoded = encoder.transform(test.category)
+    >>> train_encoded = encoder.fit_transform(train[["category"]], train.label)
+    >>> test_encoded = encoder.transform(test[["category"]])
     >>> print(train_encoded)
     [1. 1. 0. 1.]
     >>> print(test_encoded)
diff --git a/python/cuml/cuml/preprocessing/label.py b/python/cuml/cuml/preprocessing/label.py
index e7afa6cea7..590e5fc458 100644
--- a/python/cuml/cuml/preprocessing/label.py
+++ b/python/cuml/cuml/preprocessing/label.py
@@ -157,7 +157,7 @@ def __init__(
         self.sparse_output = sparse_output
         self.classes_ = None
 
-    @cuml.internals.reflect(reset=True)
+    @cuml.internals.reflect(reset="type")
     def fit(self, y) -> "LabelBinarizer":
         """
         Fit label binarizer
diff --git a/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml b/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
index 91b04a85bc..94e2d0ab85 100644
--- a/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
+++ b/python/cuml/cuml_accel_tests/upstream/scikit-learn/xfail-list.yaml
@@ -175,8 +175,6 @@
   - "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_array-randomized]"
   - "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_matrix-full]"
   - "sklearn.decomposition.tests.test_pca::test_sparse_pca_solver_error[42-csr_matrix-randomized]"
-  - "sklearn.ensemble.tests.test_forest::test_1d_input[RandomForestClassifier]"
-  - "sklearn.ensemble.tests.test_forest::test_1d_input[RandomForestRegressor]"
   - "sklearn.ensemble.tests.test_forest::test_backend_respected"
   - "sklearn.ensemble.tests.test_forest::test_class_weights[RandomForestClassifier]"
   - "sklearn.ensemble.tests.test_forest::test_estimators_samples[RandomForestClassifier-False-1]"
@@ -441,11 +439,9 @@
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',LogisticRegression())])-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Pipeline(steps=[('scaler',StandardScaler()),('final_estimator',Ridge())])-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[SGDClassifier()-check_sample_weights_invariance(kind=zeros)]"
@@ -460,21 +456,16 @@
   - "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_estimators_dtypes]"
   - "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[SpectralCoclustering()-check_methods_subset_invariance]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_classifier_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_fit2d_1feature]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_grid={'logisticregression__C':[0.1,1.0]})-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_fit2d_1feature]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_grid={'ridge__alpha':[0.1,1.0]})-check_supervised_y_no_nan]"
@@ -482,12 +473,8 @@
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})-check_fit1d]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=LogisticRegression(),param_grid={'C':[0.1,1.0]})-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_fit1d]"
-  - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[GridSearchCV(cv=2,estimator=Ridge(),param_grid={'alpha':[0.1,1.0]})-check_supervised_y_no_nan]"
@@ -495,19 +482,15 @@
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),min_resources='smallest',param_grid={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_fit_returns_self(readonly_memmap=True)]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_fit_returns_self]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_overwrite_params]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),min_resources='smallest',param_grid={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]"
@@ -519,20 +502,12 @@
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf0]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf1]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_fit1d0]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_fit1d1]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d0]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=LogisticRegression(),min_resources='smallest',param_grid={'C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_dtype_object0]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_dtype_object1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf0]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf1]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit1d0]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit1d1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_1sample0]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_1sample1]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d0]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array0]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array1]"
   - "sklearn.tests.test_common::test_search_cv[HalvingGridSearchCV(cv=2,estimator=Ridge(),min_resources='smallest',param_grid={'alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d0]"
@@ -543,20 +518,16 @@
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_fit_returns_self(readonly_memmap=True)]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_fit_returns_self]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_overwrite_params]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[HalvingRandomSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]"
@@ -564,16 +535,12 @@
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('logisticregression',LogisticRegression())]),param_distributions={'logisticregression__C':[0.1,1.0]},random_state=0)-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_1feature]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,error_score='raise',estimator=Pipeline(steps=[('pca',PCA()),('ridge',Ridge())]),param_distributions={'ridge__alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]"
@@ -581,12 +548,8 @@
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_fit1d]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=LogisticRegression(),param_distributions={'C':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_dtype_object]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_fit1d]"
-  - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_supervised_y_2d]"
   - "sklearn.tests.test_common::test_search_cv[RandomizedSearchCV(cv=2,estimator=Ridge(),param_distributions={'alpha':[0.1,1.0]},random_state=0)-check_supervised_y_no_nan]"
@@ -745,9 +708,7 @@
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[LogisticRegression()-check_sparsify_coefficients]"
@@ -905,8 +866,6 @@
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_estimators_pickle(readonly_memmap=True)]"
-  - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_regressors_train(readonly_memmap=True)]"
   - "sklearn.tests.test_common::test_estimators[KernelRidge()-check_regressors_train(readonly_memmap=True,X_dtype=float32)]"
@@ -1091,7 +1050,6 @@
   tests:
   - "sklearn.tests.test_common::test_estimators[SVR()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[SVR()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[SVR()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[SVR()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[SVR()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[SVR()-check_sample_weights_not_an_array]"
@@ -1113,12 +1071,9 @@
   - "sklearn.tests.test_common::test_estimators[DBSCAN()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[DBSCAN()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[DBSCAN()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[DBSCAN()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[DBSCAN()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_regressor_multioutput]"
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_requires_y_none]"
@@ -1126,32 +1081,25 @@
   - "sklearn.tests.test_common::test_estimators[ElasticNet()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[KMeans()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[KMeans()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[KMeans()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[KMeans()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[KMeans()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[KMeans()-check_transformer_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_classifier_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsClassifier()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[KNeighborsRegressor()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[KernelDensity()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[KernelDensity()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[KernelDensity()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[KernelDensity()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[Lasso()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[Lasso()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_regressor_multioutput]"
   - "sklearn.tests.test_common::test_estimators[Lasso()-check_requires_y_none]"
@@ -1163,9 +1111,7 @@
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[LinearRegression()-check_sample_weights_not_an_array]"
@@ -1176,10 +1122,8 @@
   - "sklearn.tests.test_common::test_estimators[PCA()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[PCA()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[PCA()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[PCA()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[PCA()-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_estimators[PCA()-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[PCA()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[PCA()-check_transformer_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_classifier_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_classifiers_multilabel_output_format_decision_function]"
@@ -1187,36 +1131,27 @@
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_classifiers_train]"
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_estimators_empty_data_messages]"
-  - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[RandomForestClassifier()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_estimators_empty_data_messages]"
-  - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[RandomForestRegressor()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[Ridge()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[Ridge()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[Ridge()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[SpectralEmbedding()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[SpectralEmbedding()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[StandardScaler()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[TSNE()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[TSNE()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[TSNE()-check_estimators_nan_inf]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_estimators_empty_data_messages]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_fit1d]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_fit2d_1feature]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_fit2d_1sample]"
-  - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[TruncatedSVD()-check_transformer_data_not_an_array]"
 - reason: test_estimators checks fail
   marker: cuml_accel_test_estimators
@@ -1241,8 +1176,8 @@
   condition: scikit-learn<1.6
   tests:
   - "sklearn.tests.test_common::test_estimators[TSNE()-check_pipeline_consistency]"
-- reason: TSNE does validation on __init__, which causes these tests to fail only on certain sklearn versions
-  marker: cuml_accel_tsne_validation_on_init
+- reason: TSNE currently errors on small n_samples
+  marker: cuml_accel_tsne_validations
   condition: scikit-learn>=1.6
   tests:
   - "sklearn.tests.test_common::test_estimators[TSNE()-check_dont_overwrite_parameters]"
@@ -1400,8 +1335,6 @@
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_classifier_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_sample_weights_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_sparsify_coefficients]"
@@ -1409,8 +1342,6 @@
   - "sklearn.tests.test_common::test_estimators[LinearSVC()-check_supervised_y_no_nan]"
   - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_dtype_object]"
   - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_estimators_nan_inf]"
-  - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_fit1d]"
-  - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_fit2d_predict1d]"
   - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_regressor_data_not_an_array]"
   - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_requires_y_none]"
   - "sklearn.tests.test_common::test_estimators[LinearSVR()-check_sample_weights_not_an_array]"
diff --git a/python/cuml/tests/test_coordinate_descent.py b/python/cuml/tests/test_coordinate_descent.py
index b180042d81..6588d5f419 100644
--- a/python/cuml/tests/test_coordinate_descent.py
+++ b/python/cuml/tests/test_coordinate_descent.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2019-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
 
@@ -274,7 +274,7 @@ def test_lasso_predict_convert_dtype(train_dtype, test_dtype):
 
 @pytest.mark.parametrize("cls", [cuml.ElasticNet, cuml.Lasso])
 def test_set_params(cls):
-    x = np.linspace(0, 1, 50)
+    x = np.linspace(0, 1, 50)[:, None]
     y = 2 * x
 
     model = cls(alpha=0.01)
diff --git a/python/cuml/tests/test_dbscan.py b/python/cuml/tests/test_dbscan.py
index 33baebe513..c5d1f3f8b8 100644
--- a/python/cuml/tests/test_dbscan.py
+++ b/python/cuml/tests/test_dbscan.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2019-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2019-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 #
 
@@ -497,7 +497,7 @@ def test_dbscan_no_calc_core_point_indices():
 
 
 def test_dbscan_on_empty_array():
-    X = np.array([])
+    X = np.array([[]])
     cuml_dbscan = cuDBSCAN()
 
     with pytest.raises(ValueError):
diff --git a/python/cuml/tests/test_label_binarizer.py b/python/cuml/tests/test_label_binarizer.py
index 640fdcef9d..78f745823f 100644
--- a/python/cuml/tests/test_label_binarizer.py
+++ b/python/cuml/tests/test_label_binarizer.py
@@ -1,4 +1,4 @@
-# SPDX-FileCopyrightText: Copyright (c) 2020-2025, NVIDIA CORPORATION.
+# SPDX-FileCopyrightText: Copyright (c) 2020-2026, NVIDIA CORPORATION.
 # SPDX-License-Identifier: Apache-2.0
 
 import cupy as cp
@@ -11,6 +11,13 @@
 from cuml.testing.utils import array_equal
 
 
+def test_label_binarizer_no_features():
+    """Ensure the features infra is never applied to LabelBinarizer"""
+    y = cp.asarray([1, 2, 1, 2, 1, 0])
+    model = LabelBinarizer().fit(y)
+    assert not hasattr(model, "n_features_in_")
+
+
 @pytest.mark.parametrize(
     "labels",
     [
diff --git a/python/cuml/tests/test_label_encoder.py b/python/cuml/tests/test_label_encoder.py
index 222d0c0afc..50913a34e0 100644
--- a/python/cuml/tests/test_label_encoder.py
+++ b/python/cuml/tests/test_label_encoder.py
@@ -20,6 +20,13 @@ def _df_to_similarity_mat(df):
     return np.pad(arr, [(arr.shape[1] - 1, 0), (0, 0)], "edge")
 
 
+def test_label_encoder_no_features():
+    """Ensure the features infra is never applied to LabelEncoder"""
+    y = cp.asarray([1, 2, 1, 2, 1, 0])
+    model = LabelEncoder().fit(y)
+    assert not hasattr(model, "n_features_in_")
+
+
 @pytest.mark.parametrize("length", [10, 1000])
 @pytest.mark.parametrize("cardinality", [5, 10, 50])
 def test_labelencoder_fit_transform(length, cardinality):
diff --git a/python/cuml/tests/test_target_encoder.py b/python/cuml/tests/test_target_encoder.py
index ac5450d267..ab1aba1489 100644
--- a/python/cuml/tests/test_target_encoder.py
+++ b/python/cuml/tests/test_target_encoder.py
@@ -16,36 +16,57 @@
 )
 
 
-def test_targetencoder_fit_transform():
-    train = cudf.DataFrame(
+def test_targetencoder_deprecated_1d_input():
+    df = cudf.DataFrame(
         {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
     )
+
+    # Warns in fit_transform
     encoder = TargetEncoder()
-    train_encoded = encoder.fit_transform(train.category, train.label)
+    with pytest.warns(FutureWarning, match="non-2-dimensional X"):
+        encoded = encoder.fit_transform(df.category, df.label)
+    answer = np.array([1.0, 1.0, 0.0, 1.0])
+    assert array_equal(encoded, answer)
+
+    # Warns in fit
+    encoder = TargetEncoder()
+    with pytest.warns(FutureWarning, match="non-2-dimensional X"):
+        encoder.fit(df.category, df.label)
+
+    # Warns in tarnsform
+    with pytest.warns(FutureWarning, match="non-2-dimensional X"):
+        encoded = encoder.transform(df.category)
+    assert array_equal(encoded, answer)
+
+
+def test_targetencoder_fit_transform():
+    train = cudf.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = cudf.Series([1, 0, 1, 1])
+    encoder = TargetEncoder()
+    train_encoded = encoder.fit_transform(train, label)
     answer = np.array([1.0, 1.0, 0.0, 1.0])
     assert array_equal(train_encoded, answer)
 
     encoder = TargetEncoder()
-    encoder.fit(train.category, train.label)
-    train_encoded = encoder.transform(train.category)
+    encoder.fit(train, label)
+    train_encoded = encoder.transform(train)
 
     assert array_equal(train_encoded, answer)
 
 
 def test_targetencoder_transform():
-    train = cudf.DataFrame(
-        {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
-    )
+    train = cudf.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = cudf.Series([1, 0, 1, 1])
     test = cudf.DataFrame({"category": ["b", "b", "a", "b"]})
     encoder = TargetEncoder()
-    encoder.fit_transform(train.category, train.label)
-    test_encoded = encoder.transform(test.category)
+    encoder.fit_transform(train, label)
+    test_encoded = encoder.transform(test)
     answer = np.array([0.5, 0.5, 1.0, 0.5])
     assert array_equal(test_encoded, answer)
 
     encoder = TargetEncoder()
-    encoder.fit(train.category, train.label)
-    test_encoded = encoder.transform(test.category)
+    encoder.fit(train, label)
+    test_encoded = encoder.transform(test)
     assert array_equal(test_encoded, answer)
 
 
@@ -58,8 +79,8 @@ def test_targetencoder_random(n_samples, dtype, stat):
     xt = cp.random.randint(0, 1000, n_samples).astype(dtype)
 
     encoder = TargetEncoder(stat=stat)
-    encoder.fit_transform(x, y)
-    test_encoded = encoder.transform(xt)
+    encoder.fit_transform(x[:, None], y)
+    test_encoded = encoder.transform(xt[:, None])
 
     df_train = cudf.DataFrame({"x": x, "y": y})
     dg = df_train.groupby("x", as_index=False).agg({"y": stat})
@@ -108,34 +129,32 @@ def test_targetencoder_newly_encountered():
     Note that there are newly-encountered values in test,
     namely, 'c' and 'd'.
     """
-    train = cudf.DataFrame(
-        {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
-    )
+    train = cudf.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = cudf.Series([1, 0, 1, 1])
     test = cudf.DataFrame({"category": ["c", "b", "a", "d"]})
     encoder = TargetEncoder()
-    encoder.fit_transform(train.category, train.label)
-    test_encoded = encoder.transform(test.category)
+    encoder.fit_transform(train, label)
+    test_encoded = encoder.transform(test)
     answer = np.array([0.75, 0.5, 1.0, 0.75])
     assert array_equal(test_encoded, answer)
 
     encoder = TargetEncoder()
-    encoder.fit(train.category, train.label)
-    test_encoded = encoder.transform(test.category)
+    encoder.fit(train, label)
+    test_encoded = encoder.transform(test)
     assert array_equal(test_encoded, answer)
 
 
 def test_one_category():
-    train = cudf.DataFrame(
-        {"category": ["a", "a", "a", "a"], "label": [3, 0, 0, 3]}
-    )
+    train = cudf.DataFrame({"category": ["a", "a", "a", "a"]})
+    label = cudf.Series([3, 0, 0, 3])
     test = cudf.DataFrame({"category": ["c", "b", "a", "d"]})
 
     encoder = TargetEncoder()
-    train_encoded = encoder.fit_transform(train.category, train.label)
+    train_encoded = encoder.fit_transform(train, label)
     answer = np.array([1.0, 2.0, 2.0, 1.0])
     assert array_equal(train_encoded, answer)
 
-    test_encoded = encoder.transform(test.category)
+    test_encoded = encoder.transform(test)
     answer = np.array([1.5, 1.5, 1.5, 1.5])
     assert array_equal(test_encoded, answer)
 
@@ -145,16 +164,14 @@ def test_targetencoder_pandas():
     Note that there are newly-encountered values in test,
     namely, 'c' and 'd'.
     """
-    train = pandas.DataFrame(
-        {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
-    )
+    train = pandas.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = pandas.Series([1, 0, 1, 1])
     test = pandas.DataFrame({"category": ["c", "b", "a", "d"]})
     encoder = TargetEncoder()
-    encoder.fit_transform(train.category, train.label)
-    test_encoded = encoder.transform(test.category)
+    encoder.fit_transform(train, label)
+    test_encoded = encoder.transform(test)
     answer = np.array([0.75, 0.5, 1.0, 0.75])
     assert array_equal(test_encoded, answer)
-    print(type(test_encoded))
     assert isinstance(test_encoded, pandas.Series)
 
 
@@ -163,15 +180,14 @@ def test_targetencoder_numpy():
     Note that there are newly-encountered values in x_test,
     namely, 3 and 4.
     """
-    x_train = np.array([1, 2, 2, 1])
+    x_train = np.array([1, 2, 2, 1])[:, None]
     y_train = np.array([1, 0, 1, 1])
-    x_test = np.array([1, 2, 3, 4])
+    x_test = np.array([1, 2, 3, 4])[:, None]
     encoder = TargetEncoder()
     encoder.fit_transform(x_train, y_train)
     test_encoded = encoder.transform(x_test)
     answer = np.array([1.0, 0.5, 0.75, 0.75])
     assert array_equal(test_encoded, answer)
-    print(type(test_encoded))
     assert isinstance(test_encoded, np.ndarray)
 
 
@@ -180,22 +196,20 @@ def test_targetencoder_cupy():
     Note that there are newly-encountered values in x_test,
     namely, 3 and 4.
     """
-    x_train = cp.array([1, 2, 2, 1])
+    x_train = cp.array([1, 2, 2, 1])[:, None]
     y_train = cp.array([1, 0, 1, 1])
-    x_test = cp.array([1, 2, 3, 4])
+    x_test = cp.array([1, 2, 3, 4])[:, None]
     encoder = TargetEncoder()
     encoder.fit_transform(x_train, y_train)
     test_encoded = encoder.transform(x_test)
     answer = np.array([1.0, 0.5, 0.75, 0.75])
     assert array_equal(test_encoded, answer)
-    print(type(test_encoded))
     assert isinstance(test_encoded, cp.ndarray)
 
 
 def test_targetencoder_smooth():
-    train = cudf.DataFrame(
-        {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
-    )
+    train = cudf.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = cudf.Series([1, 0, 1, 1])
     answers = np.array(
         [
             [1.0, 1.0, 0.0, 1.0],
@@ -207,12 +221,12 @@ def test_targetencoder_smooth():
     smooths = [0, 1, 2, 10000]
     for smooth, answer in zip(smooths, answers):
         encoder = TargetEncoder(smooth=smooth)
-        train_encoded = encoder.fit_transform(train.category, train.label)
+        train_encoded = encoder.fit_transform(train, label)
         assert array_equal(train_encoded, answer)
 
         encoder = TargetEncoder(smooth=smooth)
-        encoder.fit(train.category, train.label)
-        train_encoded = encoder.transform(train.category)
+        encoder.fit(train, label)
+        train_encoded = encoder.transform(train)
 
         assert array_equal(train_encoded, answer)
 
@@ -224,36 +238,32 @@ def test_targetencoder_customized_fold_id():
     the 2nd and 3rd sample belongs to `fold 1`
     and the 4th sample belongs to `fold 2`
     """
-    train = cudf.DataFrame(
-        {"category": ["a", "b", "b", "a"], "label": [1, 0, 1, 1]}
-    )
+    train = cudf.DataFrame({"category": ["a", "b", "b", "a"]})
+    label = cudf.Series([1, 0, 1, 1])
     fold_ids = [0, 1, 1, 2]
     encoder = TargetEncoder(split_method="customize")
-    train_encoded = encoder.fit_transform(
-        train.category, train.label, fold_ids=fold_ids
-    )
+    train_encoded = encoder.fit_transform(train, label, fold_ids=fold_ids)
     answer = np.array([1.0, 0.75, 0.75, 1.0])
     assert array_equal(train_encoded, answer)
 
     encoder = TargetEncoder(split_method="customize")
-    encoder.fit(train.category, train.label, fold_ids=fold_ids)
-    train_encoded = encoder.transform(train.category)
+    encoder.fit(train, label, fold_ids=fold_ids)
+    train_encoded = encoder.transform(train)
 
     assert array_equal(train_encoded, answer)
 
 
 def test_targetencoder_var():
-    train = cudf.DataFrame(
-        {"category": ["a", "b", "b", "b"], "label": [1, 0, 1, 1]}
-    )
+    train = cudf.DataFrame({"category": ["a", "b", "b", "b"]})
+    label = cudf.Series([1, 0, 1, 1])
     encoder = TargetEncoder(stat="var")
-    train_encoded = encoder.fit_transform(train.category, train.label)
+    train_encoded = encoder.fit_transform(train, label)
     answer = np.array([0.25, 0.0, 0.5, 0.5])
     assert array_equal(train_encoded, answer)
 
     encoder = TargetEncoder(stat="var")
-    encoder.fit(train.category, train.label)
-    train_encoded = encoder.transform(train.category)
+    encoder.fit(train, label)
+    train_encoded = encoder.transform(train)
 
     assert array_equal(train_encoded, answer)
 
@@ -289,18 +299,16 @@ def test_get_params():
 
 def test_targetencoder_median():
     train = cudf.DataFrame(
-        {
-            "category": ["a", "a", "a", "a", "b", "b", "b", "b"],
-            "label": [1, 22, 15, 17, 70, 9, 99, 56],
-        }
+        {"category": ["a", "a", "a", "a", "b", "b", "b", "b"]}
     )
+    label = cudf.Series([1, 22, 15, 17, 70, 9, 99, 56])
     encoder = TargetEncoder(stat="median")
-    train_encoded = encoder.fit_transform(train.category, train.label)
+    train_encoded = encoder.fit_transform(train, label)
     answer = np.array([17.0, 15.0, 17.0, 15.0, 56.0, 70.0, 56.0, 70.0])
     assert array_equal(train_encoded, answer)
 
     encoder = TargetEncoder(stat="median")
-    encoder.fit(train.category, train.label)
-    train_encoded = encoder.transform(train.category)
+    encoder.fit(train, label)
+    train_encoded = encoder.transform(train)
 
     assert array_equal(train_encoded, answer)
diff --git a/python/cuml/tests/test_tsne.py b/python/cuml/tests/test_tsne.py
index 2dd7a4ae54..9f247d7c1d 100644
--- a/python/cuml/tests/test_tsne.py
+++ b/python/cuml/tests/test_tsne.py
@@ -230,7 +230,7 @@ def test_tsne_large(nrows, ncols, method):
 
 def test_components_exception():
     with pytest.raises(ValueError):
-        TSNE(n_components=3).fit(np.array([]))
+        TSNE(n_components=3).fit(np.array([[]]))
 
 
 @pytest.mark.parametrize("input_type", ["cupy", "scipy"])
diff --git a/python/cuml/tests/test_validation.py b/python/cuml/tests/test_validation.py
index 34837a6cf1..ee4481cb7d 100644
--- a/python/cuml/tests/test_validation.py
+++ b/python/cuml/tests/test_validation.py
@@ -78,10 +78,28 @@ def __cuda_array_interface__(self):
     assert _get_n_features([[1, 2, 3], [3, 4, 5]]) == 3
     assert _get_n_features([np.array([1, 2])]) == 2
     assert _get_n_features([]) == 0
-    assert _get_n_features([1, 2, 3]) == 1
-    assert _get_n_features(["a", "b", "c"]) == 1
-    assert _get_n_features([b"a", b"b", b"c"]) == 1
-    assert _get_n_features([{"a": 1, "b": 2}, {"c": 3}]) == 1
+
+    with pytest.raises(ValueError, match="Expected 2D array, got 3D array"):
+        _get_n_features(np.ones((3, 2, 1)))
+
+
+@pytest.mark.parametrize(
+    "X",
+    [
+        [1, 2, 3],
+        ["a", "b", "c"],
+        [b"a", b"b", b"c"],
+        [{"a": 1, "b": 2}, {"c": 3}],
+        np.array([1, 2, 3]),
+        cp.array([1, 2, 3]),
+        pd.Series([1, 2, 3]),
+        cudf.Series([1, 2, 3]),
+    ],
+)
+def test_get_n_features_1D(X):
+    with pytest.warns(FutureWarning, match="non-2-dimensional"):
+        n_features = _get_n_features(X)
+    assert n_features == 1
 
 
 def test_get_feature_names():