scikit-learn
diff --git a/‎examples/calibration/plot_calibration_multiclass.py
Lines changed: 1 addition & 1 deletion b/‎examples/calibration/plot_calibration_multiclass.py
Lines changed: 1 addition & 1 deletion
diff --git a/‎sklearn/calibration.py
Lines changed: 26 additions & 1 deletion b/‎sklearn/calibration.py
Lines changed: 26 additions & 1 deletion
diff --git a/‎sklearn/tests/test_calibration.py
Lines changed: 16 additions & 0 deletions b/‎sklearn/tests/test_calibration.py
Lines changed: 16 additions & 0 deletions
@@ -141,7 +141,7 @@ class of an instance (red: class 1, green: class 2, blue: class 3).
 calibrated_classifier = sig_clf.calibrated_classifiers_[0]
 prediction = np.vstack([calibrator.predict(this_p)
                         for calibrator, this_p in
-                        zip(calibrated_classifier.calibrators_, p.T)]).T
+                        zip(calibrated_classifier.calibrators, p.T)]).T
 prediction /= prediction.sum(axis=1)[:, None]
 
 # Plot modifications of calibrator
 
@@ -23,7 +23,12 @@
 from .base import (BaseEstimator, ClassifierMixin, RegressorMixin, clone,
                    MetaEstimatorMixin)
 from .preprocessing import label_binarize, LabelEncoder
-from .utils import check_array, indexable, column_or_1d
+from .utils import (
+    check_array,
+    column_or_1d,
+    deprecated,
+    indexable,
+)
 from .utils.multiclass import check_classification_targets
 from .utils.fixes import delayed
 from .utils.validation import check_is_fitted, check_consistent_length
@@ -585,6 +590,16 @@ class _CalibratedClassifier:
         The method to use for calibration. Can be 'sigmoid' which
         corresponds to Platt's method or 'isotonic' which is a
         non-parametric approach based on isotonic regression.
+
+    Attributes
+    ----------
+    calibrators_ : list of fitted estimator instances
+        Same as `calibrators`. Exposed for backward-compatibility. Use
+        `calibrators` instead.
+
+        .. deprecated:: 0.24
+           `calibrators_` is deprecated from 0.24 and will be removed in
+           0.26. Use `calibrators` instead.
     """
     def __init__(self, base_estimator, calibrators, *, classes,
                  method='sigmoid'):
@@ -593,6 +608,16 @@ def __init__(self, base_estimator, calibrators, *, classes,
         self.classes = classes
         self.method = method
 
+    # TODO: Remove in 0.26
+    # mypy error: Decorated property not supported
+    @deprecated(  # type: ignore
+        "calibrators_ is deprecated in 0.24 and will be removed in 0.26. "
+        "Use calibrators instead."
+    )
+    @property
+    def calibrators_(self):
+        return self.calibrators
+
     def predict_proba(self, X):
         """Calculate calibrated probabilities.
 
 
@@ -542,3 +542,19 @@ def test_calibration_attributes(clf, cv):
         classes = LabelEncoder().fit(y).classes_
         assert_array_equal(calib_clf.classes_, classes)
         assert calib_clf.n_features_in_ == X.shape[1]
+
+
+# FIXME: remove in 0.26
+def test_calibrated_classifier_cv_deprecation(data):
+    # Check that we raise the proper deprecation warning if accessing
+    # `calibrators_` from the `_CalibratedClassifier`.
+    X, y = data
+    calib_clf = CalibratedClassifierCV(cv=2).fit(X, y)
+
+    with pytest.warns(FutureWarning):
+        calibrators = calib_clf.calibrated_classifiers_[0].calibrators_
+
+    for clf1, clf2 in zip(
+        calibrators, calib_clf.calibrated_classifiers_[0].calibrators
+    ):
+        assert clf1 is clf2