Update Tf-IDF.md

contrib/machine-learning/Tf-IDF.md

@@ -19,7 +19,7 @@ df(t) = Number of documents containing term t
 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)
-$$TF-IDF(t,d,D)=TF(t,d)×IDF(t,D)$$
+$$TF-IDF(t,d,D)=tf(t,d)×idf(t,D)$$
 
 ### Applications of TF-IDF
 TF-IDF is widely used in various applications in the different fields as follows:
@@ -74,4 +74,4 @@ By calculating TF-IDF for all terms across all documents, we can identify the mo
 
 
 ### Conclusion
-TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used technique in text mining and information retrieval for identifying the importance of words in a document relative to a collection of documents. It effectively highlights significant terms by balancing term frequency within a document and the rarity of the term across the corpus.
+TF-IDF (Term Frequency-Inverse Document Frequency) is a widely used technique in text mining and information retrieval for identifying the importance of words in a document relative to a collection of documents. It effectively highlights significant terms by balancing term frequency within a document and the rarity of the term across the corpus.