Choosing the Right N-Gram Range
Balance signal against feature blow-up.
Choosing the Right N-Gram Range is a free NLP Academy lesson on CoddyKit — lesson 4 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.
A Real Trade-Off
Picking an n-gram range is a balancing act. More context helps the model, but it can flood you with features and noise.
The Cost of Going Big
Each step up in n can multiply your vocabulary. This feature blow-up means slower training and a hungrier memory footprint.
Rare Grams Add Noise
Long n-grams often appear once and never again. These rare features rarely generalize and can nudge the model toward overfitting.
Start Small and Simple
A great first move is (1, 2): unigrams plus bigrams. It catches negation and short phrases without exploding the feature count.
vec = CountVectorizer(ngram_range=(1, 2))When Trigrams Earn Their Keep
Reach for (1, 3) only when fixed three-word phrases matter, like a product name. Otherwise the extra columns rarely pay off.
Prune With min_df
Set min_df so a gram must appear in several documents to count. This quietly removes the one-off phrases that only add noise.
vec = CountVectorizer(ngram_range=(1, 3), min_df=3)Cap It With max_features
You can also hand the vectorizer a budget. max_features keeps only the top terms by frequency, holding the matrix to a fixed width.
vec = CountVectorizer(ngram_range=(1, 2), max_features=10000)Let Data Decide
Do not guess forever. Try a few ranges and compare validation scores. The data will tell you where the gains flatten out.
Tune It Automatically
Drop ngram_range into a grid search and let scikit-learn test ranges for you, picking the option with the best cross-validated score.
params = {"vec__ngram_range": [(1, 1), (1, 2), (1, 3)]}Watch for Overfitting
If training accuracy soars but validation lags, your range may be too wide. Shrinking n often generalizes better on new text.
A Sensible Default
For most text tasks, ngram_range (1, 2) with a small min_df is a strong, fast baseline. Reach higher only when evidence demands it.
Quick Check
Your trigram model overfits and trains slowly. Which single change most directly fights the feature blow-up?
Recap: Choosing Your Range
Start at (1, 2), prune with min_df, cap with max_features, and let validation pick the winner. You can now model context with n-grams. 🚀
Frequently asked questions
Is the “Choosing the Right N-Gram Range” lesson free?
Yes — the full text of “Choosing the Right N-Gram Range” 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 “Choosing the Right N-Gram Range”?
Balance signal against feature blow-up. 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 4 of 4, so you can start here or from the beginning and move at your own pace.
How long does the “Choosing the Right N-Gram Range” 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
- Why Single Words Lose Meaning
- Bigrams and Trigrams Explained
- N-Gram Features in scikit-learn
- Choosing the Right N-Gram Range