ML之NB：利用朴素贝叶斯NB算法(TfidfVectorizer+不去除停用词)对20类新闻文本数据集进行分类预测、评估

ML之NB：利用朴素贝叶斯NB算法(TfidfVectorizer+不去除停用词)对20类新闻文本数据集进行分类预测、评估

 

目录

输出结果

设计思路

核心代码

 

 

 

 

 

 

 

输出结果

 

 

设计思路

 

 

核心代码

class TfidfVectorizer Found at: sklearn.feature_extraction.text

class TfidfVectorizer(CountVectorizer):
    """Convert a collection of raw documents to a matrix of TF-IDF features.
    
    Equivalent to CountVectorizer followed by TfidfTransformer.
    
    Read more in the :ref:`User Guide `.
    
    Parameters
    ----------
    input : string {'filename', 'file', 'content'}
    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.
    
    Otherwise the input is expected to be the sequence strings or
    bytes items are expected to be analyzed directly.
    
    encoding : string, 'utf-8' by default.
    If bytes or files are given to analyze, this encoding is used to
    decode.
    
    decode_error : {'strict', 'ignore', 'replace'}
    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
    values are 'ignore' and 'replace'.
    
    strip_accents : {'ascii', 'unicode', None}
    Remove accents during the preprocessing step.
    'ascii' is a fast method that only works on characters that have
    an direct ASCII mapping.
    'unicode' is a slightly slower method that works on any characters.
    None (default) does nothing.
    
    analyzer : string, {'word', 'char'} or callable
    Whether the feature should be made of word or character n-grams.
    
    If a callable is passed it is used to extract the sequence of features
    out of the raw, unprocessed input.
    
    preprocessor : callable or None (default)
    Override the preprocessing (string transformation) stage while
    preserving the tokenizing and n-grams generation steps.
    
    tokenizer : callable or None (default)
    Override the string tokenization step while preserving the
    preprocessing and n-grams generation steps.
    Only applies if ``analyzer == 'word'``.
    
    ngram_range : tuple (min_n, max_n)
    The lower and upper boundary of the range of n-values for different
    n-grams to be extracted. All values of n such that min_n