how can I do tf idf weighting in scikit learn?

To perform tf-idf weighting in scikit-learn, you can use the TfidfVectorizer class from the sklearn.feature_extraction.text module.

The TfidfVectorizer class converts a collection of raw documents into a matrix of tf-idf features. It works by tokenizing the documents, computing the term frequency (tf) and inverse document frequency (idf) of each term, and then multiplying the tf and idf values to get the tf-idf weight of each term.

Here's an example of how to use the TfidfVectorizer class in scikit-learn:

from sklearn.feature_extraction.text import TfidfVectorizer

# Define the documents
documents = [
  "This is the first document.",
  "This document is the second document.",
  "And this is the third one.",
  "Is this the first document?",
]

# Create the TfidfVectorizer
vectorizer = TfidfVectorizer()

# Fit the vectorizer to the documents and transform them into tf-idf vectors
vectors = vectorizer.fit_transform(documents)

# Print the tf-idf vectors
print(vectors.toarray())

This code will output a matrix of tf-idf vectors, with each row representing a document and each column representing a term. The values in the matrix are the tf-idf weights of the terms in the documents.

You can also customize the behavior of the TfidfVectorizer by setting various parameters, such as the tokenization function, the n-gram range, and the minimum and maximum document frequency of the terms.