title: What is the difference between HashingTF and CountVectorizer in Spark?
body: <p>Trying to do doc classification in Spark. I am not sure what the hashing does in HashingTF; does it sacrifice any accuracy? I doubt it, but I don't know. The spark doc says it uses the "hashing trick"... just another example of really bad/confusing naming used by engineers (I'm guilty as well). CountVectorizer also requires setting the vocabulary size, but it has another parameter, a threshold param that can be used to exclude words or tokens that appear below some threshold in the text corpus. I do not understand the difference between these two Transformers. What makes this important is the subsequent steps in the algorithm. For example, if I wanted to perform SVD on the resulting tfidf matrix, then vocabulary size will determine the size of the matrix for SVD, which impacts the running time of the code, and the model performance etc. I am having a difficulty in general finding any source about Spark Mllib beyond API documentation and really trivial examples with no depth.</p>