Understanding TF-IDF (Term Frequency-Inverse Document Frequency) Last Updated : 13 Aug, 2025 Comments Improve Suggest changes Like Article Like Report TF-IDF (Term Frequency–Inverse Document Frequency) is a statistical method used in natural language processing and information retrieval to evaluate how important a word is to a document in relation to a larger collection of documents. TF-IDF combines two components:1. Term Frequency (TF): Measures how often a word appears in a document. A higher frequency suggests greater importance. If a term appears frequently in a document, it is likely relevant to the document’s content.Term Frequency (TF)2. Inverse Document Frequency (IDF): Reduces the weight of common words across multiple documents while increasing the weight of rare words. If a term appears in fewer documents, it is more likely to be meaningful and specific.Inverse Document Frequency (IDF)This balance allows TF-IDF to highlight terms that are both frequent within a specific document and distinctive across the text document, making it a useful tool for tasks like search ranking, text classification and keyword extraction.Converting Text into vectors with TF-IDFLet's take an example where we have a corpus (a collection of documents) with three documents and our goal is to calculate the TF-IDF score for specific terms in these documents.Document 1: "The cat sat on the mat."Document 2: "The dog played in the park."Document 3: "Cats and dogs are great pets."Our goal is to calculate the TF-IDF score for specific terms in these documents. Let’s focus on the word "cat" and see how TF-IDF evaluates its importance.Step 1: Calculate Term Frequency (TF)For Document 1:The word "cat" appears 1 time.The total number of terms in Document 1 is 6 ("the", "cat", "sat", "on", "the", "mat").So, TF(cat,Document 1) = 1/6For Document 2:The word "cat" does not appear.So, TF(cat,Document 2)=0.For Document 3:The word "cat" appears 1 time.The total number of terms in Document 3 is 6 ("cats", "and", "dogs", "are", "great", "pets").So TF (cat,Document 3)=1/6In Document 1 and Document 3 the word "cat" has the same TF score. This means it appears with the same relative frequency in both documents. In Document 2 the TF score is 0 because the word "cat" does not appear.Step 2: Calculate Inverse Document Frequency (IDF)Total number of documents in the corpus (D): 3Number of documents containing the term "cat": 2 (Document 1 and Document 3).IDF(cat,D)=log \frac{3}{2} ≈0.176Step 3: Calculate TF-IDFThe TF-IDF score for "cat" is 0.029 in Document 1 and Document 3 and 0 in Document 2 that reflects both the frequency of the term in the document (TF) and its rarity across the corpus (IDF).The TF-IDF score is the product of TF and IDF: TF-IDFFor Document 1: TF-IDF (cat, Document 1, D)-0.167 * 0.176 - 0.029 For Document 2: TF-IDF(cat, Document 2, D)-0x 0.176-0 For Document 3: TF-IDF (cat, Document 3, D)-0.167 x 0.176 ~ 0.029 Implementing TF-IDF in PythonStep 1: Import modulesWe will import scikit learn for this. Python from sklearn.feature_extraction.text import TfidfVectorizer Step 2: Collect strings from documents and create a corpus Python d0 = 'Geeks for geeks' d1 = 'Geeks' d2 = 'r2j' string = [d0, d1, d2] Step 3: Get TF-IDF valuesHere we are using TfidfVectorizer() from scikit learn to perform tf-idf and apply on our courpus using fit_transform. Python tfidf = TfidfVectorizer() result = tfidf.fit_transform(string) Step 4: Display IDF values Python print('\nidf values:') for ele1, ele2 in zip(tfidf.get_feature_names_out(), tfidf.idf_): print(ele1, ':', ele2) Output:Step 5: Display TF-IDF values along with indexing Python print('\nWord indexes:') print(tfidf.vocabulary_) print('\ntf-idf value:') print(result) print('\ntf-idf values in matrix form:') print(result.toarray()) Output:OutputThe result variable consists of unique words as well as the tf-if values. It can be elaborated using the below image:From the above image the below table can be generated:DocumentWordDocument IndexWord Indextf-idf valued0for000.549d0geeks010.8355d1geeks111.000d2r2j221.000ApplicationsDocument Similarity and Clustering: By converting documents into numerical vectors TF-IDF enables comparison and grouping of related texts. This is valuable for clustering news articles, research papers or customer support tickets into meaningful categories.Text Classification: It helps in identify patterns in text for spam filtering, sentiment analysis and topic classification.Keyword Extraction: It ranks words by importance making it possible to automatically highlight key terms, generate document tags or create concise summaries.Recommendation Systems: Through comparison of textual descriptions TF-IDF supports suggesting related articles, videos or products enhancing user engagement. Understanding TF-IDF Visit Course Understanding TF-IDF TF-IDF in Action Comment More infoAdvertise with us Next Article Introduction to Machine Learning R riturajsaha Follow Improve Article Tags : Machine Learning Technical Scripter 2020 Python scikit-module python AI-ML-DS With Python +1 More Practice Tags : Machine Learningpython Similar Reads Machine Learning Tutorial Machine learning is a branch of Artificial Intelligence that focuses on developing models and algorithms that let computers learn from data without being explicitly programmed for every task. In simple words, ML teaches the systems to think and understand like humans by learning from the data.Do you 5 min read Introduction to Machine LearningIntroduction to Machine LearningMachine learning (ML) allows computers to learn and make decisions without being explicitly programmed. It involves feeding data into algorithms to identify patterns and make predictions on new data. 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