  Information retrieval systems retrieves relevant documents based on a query
submitted by the user. The documents are initially indexed and the words in the
documents are assigned weights using a weighting technique called TFIDF which
is the product of Term Frequency (TF) and Inverse Document Frequency (IDF). TF
represents the number of occurrences of a term in a document. IDF measures
whether the term is common or rare across all documents. It is computed by
dividing the total number of documents in the system by the number of documents
containing the term and then computing the logarithm of the quotient. By
default, we use base 10 to calculate the logarithm. In this paper, we are going
to test this weighting technique by using a range of log bases from 0.1 to
100.0 to calculate the IDF. Testing different log bases for vector model
weighting technique is to highlight the importance of understanding the
performance of the system at different weighting values. We use the documents
of MED, CRAN, NPL, LISA, and CISI test collections that scientists assembled
explicitly for experiments in data information retrieval systems.
