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Understanding min_df and max_df in scikit CountVectorizer


Understanding min_df and max_df in scikit CountVectorizer

By : user2950861
Date : November 17 2020, 11:58 AM
This might help you max_df is used for removing terms that appear too frequently, also known as "corpus-specific stop words". For example:
max_df = 0.50 means "ignore terms that appear in more than 50% of the documents". max_df = 25 means "ignore terms that appear in more than 25 documents".
code :


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Can you add to a CountVectorizer in scikit-learn?

Can you add to a CountVectorizer in scikit-learn?


By : James Christesen
Date : March 29 2020, 07:55 AM
seems to work fine The algorithms implemented in scikit-learn are designed to be fit on all the data at once, which is necessary for most ML algorithms (though interesting not the application that you describe), so there is no update functionality.
There is a way to get to what you want by thinking of it slightly differently though, see the following code
code :
from sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer
count_vect = CountVectorizer()
count_vect.fit_transform(["This is a test"])
print count_vect.vocabulary_
count_vect.fit_transform(["This is a test", "This is not a test"])
print count_vect.vocabulary_
{u'this': 2, u'test': 1, u'is': 0}
{u'this': 3, u'test': 2, u'is': 0, u'not': 1}
How to use the Scikit learn CountVectorizer?

How to use the Scikit learn CountVectorizer?


By : sukinieves
Date : March 29 2020, 07:55 AM
I wish this helpful for you I have a set of words for which I have to check whether they are present in the documents. , Ok. I get it. The code is given below:
code :
from sklearn.feature_extraction.text import CountVectorizer
# Counting the no of times each word(Unigram) appear in document. 
vectorizer = CountVectorizer(input='content',binary=False,ngram_range=(1,1))
# First set the vocab
vectorizer = vectorizer.fit(WordList)
# Now transform the text contained in each document i.e list of text 
Document:list
tfMatrix = vectorizer.transform(Document_List).toarray()
Is there a way to set min_df and max_df in gensim's tfidf model?

Is there a way to set min_df and max_df in gensim's tfidf model?


By : Arry
Date : March 29 2020, 07:55 AM
hope this fix your issue I am using gensim's tdidf model like so: , You can filter your dictionary with
code :
dictionary.filter_extremes(no_below=min_df, no_above=rel_max_df)
ValueError: After pruning, no terms remain. Try a lower min_df or a higher max_df

ValueError: After pruning, no terms remain. Try a lower min_df or a higher max_df


By : bstobbe
Date : March 29 2020, 07:55 AM
fixed the issue. Will look into that further From the documentation, scikit-learn, TF-IDF vectorizer,
max_df : float in range [0.0, 1.0] or int, default=1.0
max_df corresponds to documents than min_df error in Ridge classifier

max_df corresponds to documents than min_df error in Ridge classifier


By : NFSID
Date : March 29 2020, 07:55 AM
To fix the issue you can do That error is telling you that your max_df value is less than the min_df value. For example:
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