From 3a83dcfeabb4f194a96d614f9944118c4a7ac413 Mon Sep 17 00:00:00 2001
From: g0g0gadget <48335772+g0g0gadget@users.noreply.github.com>
Date: Sat, 2 Nov 2019 15:17:35 -0700
Subject: [PATCH 1/2] Doc fix

---
 sklearn/naive_bayes.py | 31 +++++++++++++++++--------------
 1 file changed, 17 insertions(+), 14 deletions(-)

diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
index be9450d7334f0..c37095c5f7051 100644
--- a/sklearn/naive_bayes.py
+++ b/sklearn/naive_bayes.py
@@ -909,7 +909,7 @@ 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.
 
@@ -943,22 +943,12 @@ 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
@@ -969,6 +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,

From 592d337846f79a53b5306c4552e08c2f7537bb07 Mon Sep 17 00:00:00 2001
From: g0g0gadget <48335772+g0g0gadget@users.noreply.github.com>
Date: Sat, 2 Nov 2019 16:30:48 -0700
Subject: [PATCH 2/2] New doc fix

---
 sklearn/naive_bayes.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/sklearn/naive_bayes.py b/sklearn/naive_bayes.py
index c37095c5f7051..0b7cbc2eaeb33 100644
--- a/sklearn/naive_bayes.py
+++ b/sklearn/naive_bayes.py
@@ -945,7 +945,8 @@ class BernoulliNB(BaseDiscreteNB):
 
     See Also
     ----------
-    MultinomialNB: The multinomial Naive Bayes classifier is suitable for classification with discrete features.
+    MultinomialNB: The multinomial Naive Bayes classifier is \
+        suitable for classification with discrete features.
 
     References
     ----------