Contents
What is Tfidf norm?
Usually, the length of a vector is calculated using the Euclidean norm – a norm is a function that assigns a strictly positive length or size to all vectors in a vector space -, which is defined by: (Source: http://processing.org/learning/pvector/)
What does Tfidf Vectorizer do?
TfidfVectorizer – Transforms text to feature vectors that can be used as input to estimator. vocabulary_ Is a dictionary that converts each token (word) to feature index in the matrix, each unique token gets a feature index. It tells you that the token ‘me’ is represented as feature number 8 in the output matrix.
What is unit norm?
If you use l2-normalization, “unit norm” essentially means that if we squared each element in the vector, and summed them, it would equal 1 . (note this normalization is also often referred to as, unit norm or a vector of length 1 or a unit vector ).
When to use TFIDF on train only or test?
When training a model it is possible to train the Tfidf on the corpus of only the training set or also on the test set. It seems not to make sense to include the test corpus when training the model, though since it is not supervised, it is also possible to train it on the whole corpus. What is better to do?
Why are tf idf features less than before?
The way to rectify this is to do the train test split before the vectorizing and the vectorizer or any preprocessor in this regard should fit on the train data only. Below is the correct way to do this: As can be expected, the number of tf-idf features are less than before because there were some unique words that are only there in the test set.
How to recalculate tf-idf at test time?
At test Time: Add new documents to corpus and recalculate TF-IDF on whole corpus. Use TF-IDF values for the new document as inputs to model for scoring.
When do you need to split data into test and train sets?
As a data scientist, reality is often on the contrary. It may so happen that you need to split 3 datasets into train and test sets, and of course, the splits should be similar. Another scenario you may face that you have a complicated dataset at hand, a 4D numpy array perhaps and you need to split it over the 3rd axis.