[FIXED] Understanding min_df and max_df in scikit CountVectorizer

Issue

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 mean? 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 (five text files)?

What are the differences 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 these two parameters. Could someone provide an explanation or example demonstrating min_df and max_df?

Solution

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.

Answered By - Kevin Markham