from sklearn.feature_extraction.text import CountVectorizer # Instantiate a CountVectorizer object vectorizer = CountVectorizer() # Get the parameters for the CountVectorizer object params = vectorizer.get_params() # Print the parameters print(params)
{'analyzer': 'word', 'binary': False, 'decode_error': 'strict', 'dtype':In this example, the CountVectorizer object is instantiated without any arguments. The get_params() method is then used to retrieve the default parameters for the object. These parameters are returned in a dictionary format. Note: The code examples were written in Python programming language and used a package/library called sklearn.feature_extraction.text., 'encoding': 'utf-8', 'input': 'content', 'lowercase': True, 'max_df': 1.0, 'max_features': None, 'min_df': 1, 'ngram_range': (1, 1), 'preprocessor': None, 'stop_words': None, 'strip_accents': None, 'token_pattern': '(?u)\\b\\w\\w+\\b', 'tokenizer': None, 'vocabulary': None}