Stop 06 · Feature Engineering & Model EvaluationTopics · 4/12

Handling Imbalanced Data

In short

When only 1 in 100 cases is the thing you care about, like fraud or a rare disease, a model can look 99% accurate while catching nothing. Here's how to spot and fix that.

Techniques (resampling, class weights, SMOTE) for datasets where one class vastly outnumbers another — critical for tasks like fraud detection and churn where the rare class matters most.

The mascot security guard watches bags roll along a conveyor belt, one of them highlighted.

Think of it like this

The airport security guard

Almost every bag at security is harmless. A guard who waves every bag through would be right 99.99% of the time, and completely useless. What matters is how many dangerous bags he catches, and how many innocent bags he stops.

Watch it happen

1,000 payments, 10 of them fraud

Legit: 990

Fraud: 10

1/5A heavily imbalanced dataset. The rare class is the one you actually care about.

The big ideas

What you need to know

Use better metrics

Precision, recall, F1 and precision-recall AUC tell the truth that accuracy hides.

Class weights

Make mistakes on the rare class cost more during training. Many sklearn models accept class_weight.

Resampling

Oversample the rare class or undersample the common one, but only inside the training data.

Tune the threshold

The default 0.5 cut-off is rarely best. Pick it based on what misses and false alarms cost.

See it in code

Walk through the code

python

1from sklearn.datasets import make_classification2from sklearn.model_selection import train_test_split3from sklearn.linear_model import LogisticRegression4from sklearn.metrics import classification_report5 6X, y = make_classification(n_samples=20_000, weights=[0.99], random_state=0)7X_tr, X_te, y_tr, y_te = train_test_split(X, y, stratify=y, random_state=0)8 9model = LogisticRegression(class_weight="balanced", max_iter=1000)10model.fit(X_tr, y_tr)11print(classification_report(y_te, model.predict(X_te), digits=3))

1/4Make a dataset where about 99% of rows are class 0.

A fake dataset with about 1% positives, a stratified split and class weights. Read the recall row for class 1.

Save yourself the pain

Common mistakes

Oops

Oversampling before splitting into train and test.

Do this instead

Split first, then resample only the training set. Otherwise copies of test rows leak into training.

Oops

Reporting 99% accuracy on a 1% fraud problem.

Do this instead

Report recall and precision for the rare class, and the trade-off you chose.

Your mission

Beat the lazy model

~30 min

Quick check

A disease affects 2% of patients. Your model has 98% accuracy. What should you check first?

Go deeper

1Practice oversampling (SMOTE) and undersampling on an imbalanced dataset.
2Learn to use `class_weight` parameters instead of resampling as a simpler first fix.
3Always evaluate imbalanced models with precision/recall/F1, never accuracy alone.