Contents
What is a hashing Vectorizer?
hashing vectorizer is a vectorizer which uses the hashing trick to find the token string name to feature integer index mapping. Conversion of text documents into matrix is done by this vectorizer where it turns the collection of documents into a sparse matrix which are holding the token occurence counts.
What is the difference between TfidfTransformer and TfidfVectorizer?
With Tfidftransformer you will compute word counts using CountVectorizer and then compute the IDF values and only then compute the Tf-idf scores. With Tfidfvectorizer you will do all three steps at once.
Does Tfidfvectorizer remove stop words?
“This is a green apple.” “This is a machine learning book.” As we can see, the word book is also removed from the list of features because we listed it as a stop word. As a result, tfidfvectorizer did accept the manually added word as a stop word and ignored the word at the time of creating the vectors.
What is the difference between tfidfvectorizer and Count vectorizer?
CountVectorizer just counts the word frequencies. Simple as that. With the TFIDFVectorizer the value increases proportionally to count, but is offset by the frequency of the word in the corpus. – This is the IDF (inverse document frequency part).
How are hashingtf and countvectorizer used in text processing?
Both HashingTF and CountVectorizer can be used to generate the term frequency vectors. HashingTF is a Transformer which takes sets of terms and converts those sets into fixed-length feature vectors. In text processing, a “set of terms” might be a bag of words. HashingTF utilizes the hashing trick.
How are hash functions used in hashing vectorizer?
Hash functions are an efficient way of mapping terms to features; it doesn’t necessarily need to be applied only to term frequencies but that’s how HashingVectorizer is employed here.
What’s the difference between feature hashing and tf.idf?
‘Mining of Massive Datasets’ by Leskovec et al has a ton of detail on both feature hashing and tf.idf, the authors made the pdf available here. The HashingVectorizer has a parameter n_features which is 1048576 by default. When hashing, they don’t actually compute a dictionary mapping terms to a unique index to use for each one.