Option ‘char_wb’ creates character n-grams only from text inside
Whether the feature should be made of word n-gram or character Parameters : input or callable, default=’word’ That does some kind of feature selection then the number of features willīe equal to the vocabulary size found by analyzing the data. If you do not provide an a-priori dictionary and you do not use an analyzer This implementation produces a sparse representation of the counts using CountVectorizer ( *, input='content', encoding='utf-8', decode_error='strict', strip_accents=None, lowercase=True, preprocessor=None, tokenizer=None, stop_words=None, token_pattern='(?u)\\b\\w\\w+\\b', ngram_range=(1, 1), analyzer='word', max_df=1.0, min_df=1, max_features=None, vocabulary=None, binary=False, dtype= ) ¶Ĭonvert a collection of text documents to a matrix of token counts.
Sklearn.feature_ ¶ class sklearn.feature_extraction.text.
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