DOC Ensures that DummyClassifier passes numpydoc validation (scikit-learn#20639)

embandera · glemaitre · web-flow · commit dde6fb594589 · 2021-08-02T11:14:27.000+02:00
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
@@ -18,7 +18,6 @@
     "DecisionTreeRegressor",
     "DictVectorizer",
     "DictionaryLearning",
-    "DummyClassifier",
     "ElasticNetCV",
     "ExtraTreeRegressor",
     "FactorAnalysis",
diff --git a/sklearn/dummy.py b/sklearn/dummy.py
@@ -84,6 +84,10 @@ class DummyClassifier(MultiOutputMixin, ClassifierMixin, BaseEstimator):
         True if the array returned from predict is to be in sparse CSC format.
         Is automatically set to True if the input y is passed in sparse format.
 
+    See Also
+    --------
+    DummyRegressor : Regressor that makes predictions using simple rules.
+
     Examples
     --------
     >>> import numpy as np
@@ -121,6 +125,7 @@ def fit(self, X, y, sample_weight=None):
         Returns
         -------
         self : object
+            Returns the instance itself.
         """
         allowed_strategies = (
             "most_frequent",
@@ -362,7 +367,7 @@ def predict_log_proba(self, X):
         Parameters
         ----------
         X : {array-like, object with finite length or shape}
-            Training data, requires length = n_samples
+            Training data.
 
         Returns
         -------
@@ -411,7 +416,6 @@ def score(self, X, y, sample_weight=None):
         -------
         score : float
             Mean accuracy of self.predict(X) wrt. y.
-
         """
         if X is None:
             X = np.zeros(shape=(len(y), 1))
