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
are be becoming better good great we will
0 0.547832 0.000000 0.000000 0.000000 0.720333 0.000000 0.425441 0.000000
1 0.444514 0.000000 0.584483 0.584483 0.000000 0.000000 0.345205 0.000000
2 0.000000 0.546454 0.000000 0.000000 0.000000 0.546454 0.322745 0.546454

That's how we learned about TF IDF scores

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