DOC Improve Tfidf docstring (scikit-learn#26697)

Author: jona-sassenhagen

sklearn/feature_extraction/text.py

@@ -633,7 +633,7 @@ class HashingVectorizer(
         'ascii' is a fast method that only works on characters that have
         a direct ASCII mapping.
         'unicode' is a slightly slower method that works on any character.
-        None (default) does nothing.
+        None (default) means no character normalization is performed.
 
         Both 'ascii' and 'unicode' use NFKD normalization from
         :func:`unicodedata.normalize`.
@@ -964,7 +964,7 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
         'ascii' is a fast method that only works on characters that have
         a direct ASCII mapping.
         'unicode' is a slightly slower method that works on any characters.
-        None (default) does nothing.
+        None (default) means no character normalization is performed.
 
         Both 'ascii' and 'unicode' use NFKD normalization from
         :func:`unicodedata.normalize`.
@@ -1786,7 +1786,7 @@ class TfidfVectorizer(CountVectorizer):
         'ascii' is a fast method that only works on characters that have
         a direct ASCII mapping.
         'unicode' is a slightly slower method that works on any characters.
-        None (default) does nothing.
+        None (default) means no character normalization is performed.
 
         Both 'ascii' and 'unicode' use NFKD normalization from
         :func:`unicodedata.normalize`.
@@ -1881,7 +1881,8 @@ class TfidfVectorizer(CountVectorizer):
     binary : bool, default=False
         If True, all non-zero term counts are set to 1. This does not mean
         outputs will have only 0/1 values, only that the tf term in tf-idf
-        is binary. (Set idf and normalization to False to get 0/1 outputs).
+        is binary. (Set `binary` to True, `use_idf` to False and
+        `norm` to None to get 0/1 outputs).
 
     dtype : dtype, default=float64
         Type of the matrix returned by fit_transform() or transform().