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sklearn.feature_extraction.text.CountVectorizer — scikit-learn 1.4.0 documentation

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If you do not provide an a-priori dictionary and you do not use an analyzer that does some kind of feature selection then the number of features will be equal to the vocabulary size found by analyzing the data. For an efficiency comparison of the different feature extractors, see FeatureHasher and DictVectorizer Comparison. Read more in the User Guide. If 'filename', the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. If 'file', the sequence items must have a ‘read’ method (file-like object) that is called to fetch the bytes in memory. If 'content', the input is expected to be a sequence of items that can be of type string or byte. If bytes or files are given to analyze, this encoding is used to decode. Instruction on what to do if a byte sequence is given to analyze that contains characters not of the given encoding. By default, it is ‘strict’, meaning that a UnicodeDecodeError will be raised. Other val

CountVectorizer # class sklearn.feature_extraction.text. 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 = <class 'numpy.int64'> ) [source] # Convert a collection of text documents to a matrix of token counts. This implementation produces a sparse representation of the counts usin

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