Minor rewording.

vishaalkapoor · vishaalkapoor · commit cf42aa8fedf5 · 2019-01-28T16:58:31.000-08:00
diff --git a/sklearn/feature_extraction/text.py b/sklearn/feature_extraction/text.py
@@ -1147,12 +1147,13 @@ class TfidfTransformer(BaseEstimator, TransformerMixin):
     corpus.
 
     The formula that is used to compute the tf-idf for a term t of a document d
-    tf-idf(t, d) = tf(t, d) * idf(t), and the idf is computed as idf(t) = log [
-    n / df(t) ] + 1 (if ``smooth_idf=False``), where n is the total number of
-    documents and df(t) is the document frequency of t; the document frequency
-    is the number of documents that contain the term t. The effect of adding
-    "1" to the idf in the equation above is that terms with zero idf, i.e.,
-    terms  that occur in all documents in a training set, will not be entirely
+    in a document set is tf-idf(t, d) = tf(t, d) * idf(t), and the idf is
+    computed as idf(t) = log [ n / df(t) ] + 1 (if ``smooth_idf=False``), where
+    n is the total number of documents in the document set and df(t) is the
+    document frequency of t; the document frequency is the number of documents
+    in the document set that contain the term t. The effect of adding "1" to
+    the idf in the equation above is that terms with zero idf, i.e., terms
+    that occur in all documents in a training set, will not be entirely
     ignored.
     (Note that the idf formula above differs from the standard textbook
     notation that defines the idf as
