Term Frequency and Inverse Document Frequency
The two halves of the score.
Term Frequency and Inverse Document Frequency is a free NLP Academy lesson on CoddyKit — lesson 2 of 4. You can read the complete lesson below for free — then practise it hands-on in the browser with a built-in code editor and a 24/7 AI tutor. It is part of the NLP Academy learning path, one of 4 lessons in the course, and your progress syncs across the web and the CoddyKit app.
Two Halves of One Score
TF-IDF is built from two pieces that multiply together: term frequency and inverse document frequency. Each half fixes a different weakness of raw counts.
Term Frequency, Plainly
Term frequency measures how often a word appears inside one document. More mentions of a word suggest that document leans toward that topic.
Normalizing TF
We often divide a word's count by the document length. This normalization stops long documents from looking important just because they have more words.
count = 3
doc_length = 50
tf = count / doc_length
print(round(tf, 3))TF Alone Isn't Enough
By itself, term frequency still rewards filler words that appear a lot. We need a second factor to punish words that show up everywhere.
Document Frequency
Document frequency counts how many documents contain a word at all. A high document frequency means the word is common across your whole collection.
Flip It: Inverse
We want rare words to score high, so we invert that count. Inverse document frequency rises when a word appears in few documents and falls when it is everywhere.
The Log Tames It
IDF uses a logarithm so the values do not explode for very rare words. The log keeps the scale smooth and comparable across terms.
import math
total_docs = 1000
docs_with_word = 10
idf = math.log(total_docs / docs_with_word)
print(round(idf, 3))Multiply Them Together
The final score is simply TF times IDF. A word wins only if it is frequent here and rare elsewhere, which is exactly the combination we wanted.
tf = 0.06
idf = 4.6
tfidf = tf * idf
print(round(tfidf, 3))What Scores High
A topic word like neuroscience in one article gets a high score. A word like the gets crushed because its IDF is nearly zero.
What Scores Low
Words appearing in almost every document earn tiny weights. TF-IDF automatically demotes this background noise without any manual stopword list.
Why It Works So Well
TF-IDF balances local importance against global rarity in one clean formula. That balance is why this weighting stayed a search and text staple for decades. ⭐
Quick Check
What does the inverse document frequency part actually reward?
Recap
TF measures local frequency, IDF rewards global rarity, and their product is TF-IDF. Together they spotlight words that truly define a document. ✅
Frequently asked questions
Is the “Term Frequency and Inverse Document Frequency” lesson free?
Yes — the full text of “Term Frequency and Inverse Document Frequency” is free to read here on the web, and the NLP Academy course includes 4 lessons in total. To practise it interactively (a built-in code editor and a 24/7 AI tutor) and unlock the rest of the NLP Academy course, upgrade to CoddyKit PRO.
What will I learn in “Term Frequency and Inverse Document Frequency”?
The two halves of the score. You practise NLP Academy with hands-on code you run directly in the browser, and a 24/7 AI tutor answers your questions as you work through the lesson.
Do I need any experience to start NLP Academy?
No prior experience is required. NLP Academy on CoddyKit is structured for beginners through advanced learners; this is — lesson 2 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Term Frequency and Inverse Document Frequency” lesson take?
Most CoddyKit lessons take about 5–10 minutes. Each one is bite-sized and interactive, so you make steady progress and pick up exactly where you left off across the web and the app.
Can I write and run code in this NLP Academy lesson?
Yes. Every NLP Academy lesson includes a built-in code editor, so you write and run real code right in your browser and get instant AI feedback — no local setup required.
All lessons in this course
- The Problem With Raw Counts
- Term Frequency and Inverse Document Frequency
- TF-IDF With scikit-learn
- Finding the Most Important Words