DOC Documented attributes in QuadraticDiscriminantAnalysis, RadiusNeighborsClassifier and KNeighborsClassifier, (#14374)

abhishek-jana · thomasjpfan · commit 786eeb71122e · 2019-07-23T20:58:48.000-04:00
diff --git a/sklearn/discriminant_analysis.py b/sklearn/discriminant_analysis.py
@@ -611,6 +611,9 @@ class QuadraticDiscriminantAnalysis(BaseEstimator, ClassifierMixin):
         of the Gaussian distributions along its principal axes, i.e. the
         variance in the rotated coordinate system.
 
+    classes_ : array-like, shape (n_classes,)
+        Unique class labels.
+
     Examples
     --------
     >>> from sklearn.discriminant_analysis import QuadraticDiscriminantAnalysis
diff --git a/sklearn/neighbors/classification.py b/sklearn/neighbors/classification.py
@@ -82,6 +82,26 @@ class KNeighborsClassifier(NeighborsBase, KNeighborsMixin,
         for more details.
         Doesn't affect :meth:`fit` method.
 
+    Attributes
+    ----------
+    classes_ : array of shape = (n_classes,)
+        Class labels known to the classifier
+
+    effective_metric_ : string or callble
+        The distance metric used. It will be same as the `metric` parameter
+        or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
+        'minkowski' and `p` parameter set to 2.
+
+    effective_metric_params_ : dict
+        Additional keyword arguments for the metric function. For most metrics
+        will be same with `metric_params` parameter, but may also contain the
+        `p` parameter value if the `effective_metric_` attribute is set to
+        'minkowski'.
+
+    outputs_2d_ : bool
+        False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
+        otherwise True.
+
     Examples
     --------
     >>> X = [[0], [1], [2], [3]]
@@ -296,6 +316,26 @@ class RadiusNeighborsClassifier(NeighborsBase, RadiusNeighborsMixin,
         ``-1`` means using all processors. See :term:`Glossary <n_jobs>`
         for more details.
 
+    Attributes
+    ----------
+    classes_ : array of shape = (n_classes,)
+        Class labels known to the classifier.
+
+    effective_metric_ : string or callble
+        The distance metric used. It will be same as the `metric` parameter
+        or a synonym of it, e.g. 'euclidean' if the `metric` parameter set to
+        'minkowski' and `p` parameter set to 2.
+
+    effective_metric_params_ : dict
+        Additional keyword arguments for the metric function. For most metrics
+        will be same with `metric_params` parameter, but may also contain the
+        `p` parameter value if the `effective_metric_` attribute is set to
+        'minkowski'.
+
+    outputs_2d_ : bool
+        False when `y`'s shape is (n_samples, ) or (n_samples, 1) during fit
+        otherwise True.
+
     Examples
     --------
     >>> X = [[0], [1], [2], [3]]
