Resampling and Class Weights
Rebalance with code, not more data.
Resampling and Class Weights 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 Ways to Rebalance
You can fix skew by changing the data with resampling, or by changing the math with class weights. Both nudge attention to the rare class.
Oversampling the Minority
Oversampling copies or synthesizes more rare examples so the classes meet in the middle, giving the model more to learn from.
Undersampling the Majority
Undersampling drops some majority examples instead. It is fast and lean, but you risk throwing away useful signal.
SMOTE for Text Features
SMOTE invents new minority points between real ones in feature space, rather than duplicating, so the model sees fresh variety.
from imblearn.over_sampling import SMOTE
X_bal, y_bal = SMOTE().fit_resample(X, y)Resample Training Only
Always rebalance the training set alone. Touching your test set fakes good scores and hides the real performance.
The Weighting Idea
Instead of moving data, you can make each rare mistake hurt more. Higher class weights push the loss to respect the minority.
Balanced in One Line
Many scikit-learn models accept class_weight. Set it to balanced and weights are computed inversely to class frequency.
clf = LogisticRegression(class_weight='balanced')How Balanced Weights Work
Balanced weighting scales each class by the inverse of its frequency, so a rare label effectively counts many times more.
Weights vs Resampling
Class weights are cheap and keep your data intact, while resampling can capture richer patterns. Try weights first.
Watch for Overfitting
Aggressive oversampling can make a model memorize rare examples. Validate on untouched data to catch this overfitting early.
Pick One, Measure It
There is no universal winner. Choose a method, then judge it by minority recall, not by raw accuracy. 🎯
Quick Check
Pick the safest rebalancing habit.
Recap
Fight skew with resampling or class weights, rebalance training data only, and judge results on minority recall. ✅
Frequently asked questions
Is the “Resampling and Class Weights” lesson free?
Yes — the full text of “Resampling and Class Weights” 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 “Resampling and Class Weights”?
Rebalance with code, not more data. 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 “Resampling and Class Weights” 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 Rare Classes Get Ignored
- Resampling and Class Weights
- Choosing Threshold and Metric
- End-to-End Imbalanced Pipeline