FAQ: Term Frequency–Inverse Document Frequency - Converting Bag-of-Words to Tf-idf

This community-built FAQ covers the “Converting Bag-of-Words to Tf-idf” exercise from the lesson “Term Frequency–Inverse Document Frequency”.

Paths and Courses
This exercise can be found in the following Codecademy content:

Natural Language Processing

FAQs on the exercise Converting Bag-of-Words to Tf-idf

There are currently no frequently asked questions associated with this exercise – that’s where you come in! You can contribute to this section by offering your own questions, answers, or clarifications on this exercise. Ask or answer a question by clicking reply (reply) below.

If you’ve had an “aha” moment about the concepts, formatting, syntax, or anything else with this exercise, consider sharing those insights! Teaching others and answering their questions is one of the best ways to learn and stay sharp.

Join the Discussion. Help a fellow learner on their journey.

Ask or answer a question about this exercise by clicking reply (reply) below!
You can also find further discussion and get answers to your questions over in Language Help.

Agree with a comment or answer? Like (like) to up-vote the contribution!

Need broader help or resources? Head to Language Help and Tips and Resources. If you are wanting feedback or inspiration for a project, check out Projects.

Looking for motivation to keep learning? Join our wider discussions in Community

Learn more about how to use this guide.

Found a bug? Report it online, or post in Bug Reporting

Have a question about your account or billing? Reach out to our customer support team!

None of the above? Find out where to ask other questions here!

It might be helpful to make clearer the distinction between TfidfTransformer and TfidfVectorizer, and when to use each.

Is it right to say that the Transformer is used on frequency tables (so the table must be made before using it), and the Vectorizer is used on a corpus/documents (so it does the frequency table step for you automatically)?

2 Likes