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https://issues.apache.org/jira/browse/SPARK-3614?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=14143898#comment-14143898
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RJ Nowling commented on SPARK-3614:
-----------------------------------
It could lead to over-fitting and thus mis-predictions. In such cases, it may
be valuable to exclude overly-specific terms.
> Filter on minimum occurrences of a term in IDF
> -----------------------------------------------
>
> Key: SPARK-3614
> URL: https://issues.apache.org/jira/browse/SPARK-3614
> Project: Spark
> Issue Type: Improvement
> Components: MLlib
> Reporter: Jatinpreet Singh
> Assignee: RJ Nowling
> Priority: Minor
> Labels: TFIDF
>
> The IDF class in MLlib does not provide the capability of defining a minimum
> number of documents a term should appear in the corpus. The idea is to have a
> cutoff variable which defines this minimum occurrence value, and the terms
> which have lower frequency are ignored.
> Mathematically,
> IDF(t,D)=log( (|D|+1)/(DF(t,D)+1) ), for DF(t,D) >=minimumOccurance
> where,
> D is the total number of documents in the corpus
> DF(t,D) is the number of documents that contain the term t
> minimumOccurance is the minimum number of documents the term appears in the
> document corpus
> This would have an impact on accuracy as terms that appear in less than a
> certain limit of documents, have low or no importance in TFIDF vectors.
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