![]() They basically convert documents into a numerical representation which can be fed directly or with further processing into other algorithms like LDA, MinHash for Jaccard Distance, Cosine Distance. HashingTF and CountVectorizer are the two popular alogoritms which used to generate term frequency vectors. How it affects downstream models depends on a particular use case and data. CountVectorizer discards infrequent tokens. Counting depends on a size of the vector, training corpus and a document.Ī source of the information loss - in case of HashingTF it is dimensionality reduction with possible collisions. Hashing depends on a size of the vector, hashing function and a document. In case of unigram language model it is usually not a problem but in case of higher n-grams it can be prohibitively expensive or not feasible. CountVectorizer requires additional scan over the data to build a model and additional memory to store vocabulary (index). Memory and computational overhead - HashingTF requires only a single data scan and no additional memory beyond original input and vector. As a consequence models created using hashed input can be much harder to interpret and monitor. From the other hand count vector with model (index) can be used to restore unordered input. Partially reversible (CountVectorizer) vs irreversible (HashingTF) - since hashing is not reversible you cannot restore original input from a hash vector. Stackoverflow TF: Both HashingTF and CountVectorizer can be used to generate the term frequency vectors. Term frequency-inverse document frequency (TF-IDF) is a feature vectorization method widely used in text mining to reflect the importance of a term to a document in the corpus.
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