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DOC make max_df range consistent in CountVectorizer #25489

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Merged
merged 3 commits into from
Feb 10, 2023
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Merged

DOC make max_df range consistent in CountVectorizer #25489

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max_df range
rprkh Jan 26, 2023
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rprkh Feb 9, 2023
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Merge branch 'main' into doc_maxdf
rprkh Feb 9, 2023
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12 changes: 6 additions & 6 deletions sklearn/feature_extraction/text.py
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Expand Up @@ -996,9 +996,9 @@ class CountVectorizer(_VectorizerMixin, BaseEstimator):
will be removed from the resulting tokens.
Only applies if ``analyzer == 'word'``.

If None, no stop words will be used. max_df can be set to a value
in the range [0.7, 1.0) to automatically detect and filter stop
words based on intra corpus document frequency of terms.
If None, no stop words will be used. In this case, setting `max_df`
to a higher value, such as in the range (0.7, 1.0), can automatically detect
and filter stop words based on intra corpus document frequency of terms.

token_pattern : str or None, default=r"(?u)\\b\\w\\w+\\b"
Regular expression denoting what constitutes a "token", only used
Expand Down Expand Up @@ -1833,9 +1833,9 @@ class TfidfVectorizer(CountVectorizer):
will be removed from the resulting tokens.
Only applies if ``analyzer == 'word'``.

If None, no stop words will be used. max_df can be set to a value
in the range [0.7, 1.0) to automatically detect and filter stop
words based on intra corpus document frequency of terms.
If None, no stop words will be used. In this case, setting `max_df`
to a higher value, such as in the range (0.7, 1.0), can automatically detect
and filter stop words based on intra corpus document frequency of terms.

token_pattern : str, default=r"(?u)\\b\\w\\w+\\b"
Regular expression denoting what constitutes a "token", only used
Expand Down