DOC Ensure HuberRegressor passes numpydoc validation (#21062)

EricEllwanger · frellwan · glemaitre · web-flow · commit 7766920ed918 · 2021-09-17T11:48:33.000+02:00
Co-authored-by: frellwan &lt;frellwan@hotmail.com&gt;
Co-authored-by: Guillaume Lemaitre &lt;g.lemaitre58@gmail.com&gt;
diff --git a/maint_tools/test_docstrings.py b/maint_tools/test_docstrings.py
@@ -9,7 +9,6 @@
 
 # List of modules ignored when checking for numpydoc validation.
 DOCSTRING_IGNORE_LIST = [
-    "HuberRegressor",
     "IterativeImputer",
     "KNNImputer",
     "LabelPropagation",
diff --git a/sklearn/linear_model/_huber.py b/sklearn/linear_model/_huber.py
@@ -205,6 +205,19 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
         A boolean mask which is set to True where the samples are identified
         as outliers.
 
+    See Also
+    --------
+    RANSACRegressor : RANSAC (RANdom SAmple Consensus) algorithm.
+    TheilSenRegressor : Theil-Sen Estimator robust multivariate regression model.
+    SGDRegressor : Fitted by minimizing a regularized empirical loss with SGD.
+
+    References
+    ----------
+    .. [1] Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics
+           Concomitant scale estimates, pg 172
+    .. [2] Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
+           https://statweb.stanford.edu/~owen/reports/hhu.pdf
+
     Examples
     --------
     >>> import numpy as np
@@ -227,13 +240,6 @@ class HuberRegressor(LinearModel, RegressorMixin, BaseEstimator):
     Huber coefficients: [17.7906... 31.0106...]
     >>> print("Linear Regression coefficients:", linear.coef_)
     Linear Regression coefficients: [-1.9221...  7.0226...]
-
-    References
-    ----------
-    .. [1] Peter J. Huber, Elvezio M. Ronchetti, Robust Statistics
-           Concomitant scale estimates, pg 172
-    .. [2] Art B. Owen (2006), A robust hybrid of lasso and ridge regression.
-           https://statweb.stanford.edu/~owen/reports/hhu.pdf
     """
 
     def __init__(
@@ -271,6 +277,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Fitted `HuberRegressor` estimator.
         """
         X, y = self._validate_data(
             X,
