TF IDF scores
TF IDF (term frequency-inverse document frequency) is a way to find important features and preprocess text data for building machine learning models.
Full form of TF is term frequency. It is the count of word “x” in a sentence.
Full form of IDF is inverse document frequency. Document frequency is the number of documents which contain the word “x”.
Natural language processing (NLP) uses tf-idf technique to convert text documents to a machine understandable form. Each sentence is a document and words in the sentence are tokens. Tfidf vectorizer creates a matrix with documents and token scores therefore it is also known as document term matrix (dtm).
# Imports import numpy as np import pandas as pd import os from sklearn.feature_extraction.text import TfidfVectorizer # Let's create sample data data = ['We are good', 'We are becoming better', 'We will be great'] # Instantiate count vectorizer tfvec = TfidfVectorizer() tdf = tfvec.fit_transform(data) bow = pd.DataFrame(tdf.toarray(), columns = tfvec.get_feature_names()) bow
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