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@ -19,7 +19,7 @@ df(t) = Number of documents containing term t
N = Total number of documents N = Total number of documents
* TF-IDF: The product of TF and IDF, providing a balanced measure that accounts for both the frequency of terms in a document and their rarity across the corpus. The tf-idf weight consists of two terms :- Normalized Term Frequency (tf) and Inverse Document Frequency (idf) * TF-IDF: The product of TF and IDF, providing a balanced measure that accounts for both the frequency of terms in a document and their rarity across the corpus. The tf-idf weight consists of two terms :- Normalized Term Frequency (tf) and Inverse Document Frequency (idf)
$$TF-IDF(t,d,D)=TF(t,d)×IDF(t,D)$$ $$TF-IDF(t,d,D)=tf(t,d)×idf(t,D)$$
### Applications of TF-IDF ### Applications of TF-IDF
TF-IDF is widely used in various applications in the different fields as follows: TF-IDF is widely used in various applications in the different fields as follows: