Text Classification with TF-IDF Vectorizer
What is TF-IDF Vectorizer and how is it used in text classification?
TF-IDF Vectorizer is a popular technique for converting text documents into numerical vectors. It assigns weights to each word based on its frequency in a document relative to its frequency across all documents in the dataset.
Answer:
TF-IDF (Term Frequency-Inverse Document Frequency) Vectorizer is a tool commonly used in natural language processing for text classification tasks. It represents text data as numerical features for machine learning algorithms. The TF-IDF value for a term in a document is calculated based on the term frequency and inverse document frequency.
Explanation:
TF-IDF Vectorizer works by tokenizing text data, converting it into a sparse matrix where each row represents a document and each column represents a unique term in the vocabulary. The values in the matrix are the TF-IDF scores of each term in the document.
Term Frequency (TF) is the number of times a term appears in a document, while Inverse Document Frequency (IDF) is a measure of how important a term is across all documents. The product of TF and IDF forms the TF-IDF value, which helps in capturing the significance of a term in a document relative to the entire corpus.
TF-IDF Vectorizer is often used in text classification tasks such as sentiment analysis, spam detection, and topic categorization. It enables machine learning models to understand the content of text data by representing it in a format suitable for numerical analysis.
By using TF-IDF Vectorizer, text data can be transformed into feature vectors that capture the unique characteristics of each document. These vectors can then be fed into classification algorithms to train models for predicting the categories of new text inputs.