The Problem With Raw Counts
Why frequent words can mislead.
The Problem With Raw Counts is a free NLP Academy lesson on CoddyKit — lesson 1 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.
Counts Got Us Started
Bag-of-words turned text into numbers by counting each word. It works, but raw counts quietly mislead your model in ways worth fixing.
Frequent Words Dominate
The most common words in a document are usually the least useful. Their high frequency drowns out the rare words that actually carry meaning.
Meet the Filler Words
Words like the, is, and of appear constantly across every text. These filler words tell you almost nothing about what a document is really about.
A Quick Count Example
Count the words in this sentence and the is already the loudest. Notice how the most frequent token is also the least informative one. 🔍
text = "the cat sat on the mat"
counts = {}
for w in text.split():
counts[w] = counts.get(w, 0) + 1
print(counts)Long Documents Cheat
A longer document naturally has bigger counts everywhere. Raw numbers reward length, not relevance, so big documents look artificially important.
Rare Words Are Gold
A word that shows up in only one document is a strong clue about that document. Yet raw counts treat this rare signal the same as common noise.
We Need Two Signals
Good weighting asks two things: how often a word appears here, and how rare it is everywhere. Counts only answer the first question.
Distinctive Beats Frequent
We want to reward words that are distinctive to a document, not merely frequent. Distinctiveness is what separates a topic word from background noise.
Enter TF-IDF
The classic fix is a score called TF-IDF. It boosts words that are frequent in one document but rare across the whole collection.
Same Pipeline, Better Numbers
You still tokenize and build a vocabulary as before. TF-IDF just replaces raw counts with smarter weights in the same matrix shape.
Why This Matters
Better weights mean search, clustering, and classifiers focus on the right words. Fixing raw counts is the single biggest easy upgrade for text features.
Quick Check
Why are raw word counts a weak way to weight text?
Recap
Raw counts reward frequency and length, not relevance. You saw why we need a smarter score, setting up TF-IDF to weight words by how distinctive they are. ✅
Frequently asked questions
Is the “The Problem With Raw Counts” lesson free?
Yes — the full text of “The Problem With Raw Counts” 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 “The Problem With Raw Counts”?
Why frequent words can mislead. 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 1 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “The Problem With Raw Counts” 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