it possible someone explain "Tf is dependent on term and document" and "IDF is just dependent on the term" with an example ?
Suppose that we have these two documents:
d_1: "Tf is dependent on term and document"
d_2: "IDF is just dependent on the term"
The count of terms in each document is as follows:
d_1:
{Tf: 1, is: 1, dependent: 1, on: 1, term: 1, and: 1, document: 1}
d_2:
{IDF: 1, is: 1, just: 1, dependent: 1, on: 1, the: 1, term: 1}
The term frequencies (i.e., the ratio of times that term t appears in document d to the total count of terms of that document) for term "on" are:
tf(on, d_1) = 1 / 7
tf(on, d_2) = 1 / 7
For calculating the term frequency of a term, you must specifiy which document you are talking about. tf(on, d_1) = 1/7 tells you that 1/7 of all words in d_1 is "on".
The inverse document frequency (logarithm of ratio of documents that include the word "on") is:
idf(on) = log(2/2) = 0
As you see, the idf is constant for all documents in this corpus of two documents. It's just a measure of how common a term is in a set of documents. idf(on) = 0 tells you that "on" is not special at all and it appears in all documents.