Doc numpydoc validation for docstrings in BernoulliNB (#15513)

LauraLangdon · rth · commit 75e9bc0bf6b1 · 2019-11-06T21:22:48.000+01:00
diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
@@ -908,7 +908,7 @@ class BernoulliNB(_BaseDiscreteNB):
         Threshold for binarizing (mapping to booleans) of sample features.
         If None, input is presumed to already consist of binary vectors.
 
-    fit_prior : boolean, optional (default=True)
+    fit_prior : bool, optional (default=True)
         Whether to learn class prior probabilities or not.
         If false, a uniform prior will be used.
 
@@ -942,22 +942,13 @@ class BernoulliNB(_BaseDiscreteNB):
     classes_ : array of shape (n_classes,)
         The classes labels.
 
-    Examples
-    --------
-    >>> import numpy as np
-    >>> rng = np.random.RandomState(1)
-    >>> X = rng.randint(5, size=(6, 100))
-    >>> Y = np.array([1, 2, 3, 4, 4, 5])
-    >>> from sklearn.naive_bayes import BernoulliNB
-    >>> clf = BernoulliNB()
-    >>> clf.fit(X, Y)
-    BernoulliNB()
-    >>> print(clf.predict(X[2:3]))
-    [3]
+    See Also
+    ----------
+    MultinomialNB: The multinomial Naive Bayes classifier is \
+        suitable for classification with discrete features.
 
     References
     ----------
-
     C.D. Manning, P. Raghavan and H. Schuetze (2008). Introduction to
     Information Retrieval. Cambridge University Press, pp. 234-265.
     https://nlp.stanford.edu/IR-book/html/htmledition/the-bernoulli-model-1.html
@@ -968,6 +959,19 @@ class BernoulliNB(_BaseDiscreteNB):
 
     V. Metsis, I. Androutsopoulos and G. Paliouras (2006). Spam filtering with
     naive Bayes -- Which naive Bayes? 3rd Conf. on Email and Anti-Spam (CEAS).
+
+    Examples
+    --------
+    >>> import numpy as np
+    >>> rng = np.random.RandomState(1)
+    >>> X = rng.randint(5, size=(6, 100))
+    >>> Y = np.array([1, 2, 3, 4, 4, 5])
+    >>> from sklearn.naive_bayes import BernoulliNB
+    >>> clf = BernoulliNB()
+    >>> clf.fit(X, Y)
+    BernoulliNB()
+    >>> print(clf.predict(X[2:3]))
+    [3]
     """
 
     def __init__(self, alpha=1.0, binarize=.0, fit_prior=True,
