python - Understanding min_df and max_df in scikit CountVectorizer

Question

I have five text files that I input to a CountVectorizer. When specifying min_df and max_df to the CountVectorizer instance what does the min/max document frequency exactly means? Is it the frequency of a word in its particular text file or is it the frequency of the word in the entire overall corpus (5 txt files)?

How is it different when min_df and max_df are provided as integers or as floats?

The documentation doesn't seem to provide a thorough explanation nor does it supply an example to demonstrate the use of min_df and/or max_df. Could someone provide an explanation or example demonstrating min_df or max_df.

Answer 1

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".

The default max_df is 1.0, which means "ignore terms that appear in more than 100% of the documents". Thus, the default setting does not ignore any terms.

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min_df is used for removing terms that appear too infrequently. For example:

• min_df = 0.01 means "ignore terms that appear in less than 1% of the documents".
• min_df = 5 means "ignore terms that appear in less than 5 documents".

The default min_df is 1, which means "ignore terms that appear in less than 1 document". Thus, the default setting does not ignore any terms.