What is Tf-Idf ratio?

This recipe explains what is Tf-Idf ratio

Recipe Objective

What is Tf-Idf ratio? As we have discussed earlier only what is TF i.e Term Frequency and what is IDF i.e Inverse Document Frequency. So lets understand this with a example in which we are going to use TF-IDF Vectorizer. TF-IDF Vectorizer is a measure of originality of a word by comparing the number of times a word appears in document with the number of documents the word appears in. formula for TF-IDF is:

TF-IDF = TF(t, d) x IDF(t), where, TF(t, d) = Number of times term "t" appears in a document "d". IDF(t) = Inverse document frequency of the term t.

The TfidfVectorizer converts a collection of raw documents into a matrix of TF-IDF features.

Learn to use RNN for Text Classification with Source Code

Step 1 - Import the necessary libraries

import pandas as pd from sklearn.feature_extraction.text import TfidfVectorizer

Step 2 - Store the Tfidf vectorizer in a variable

tfidf_vect = TfidfVectorizer()

Step 3 - Take the Sample text

text1 = "jack wants to play football" text2 = "Heena also loves to play football"

Step 4 - fit_transform the text and get the feature names

vectors = tfidf_vect.fit_transform([text1, text2]) feature_names = tfidf_vect.get_feature_names()

Step 5 - Convert the vectors to dense

dense = vectors.todense()

Step 6 - Convert the dense to list and then to DataFrame

denselist = dense.tolist() df = pd.DataFrame(denselist, columns=feature_names)

Step 7 - Print the output

df

	also		football	heena		jack		loves		play		to	   	wants
0	0.000000	0.379303	0.000000	0.533098	0.000000	0.379303	0.379303	0.533098
1	0.470426	0.334712	0.470426	0.000000	0.470426	0.334712	0.334712	0.000000

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