Handling Imbalanced Data

Fix unbalanced datasets.

What is Imbalanced Data?

When one class has way more examples than others.

Example: Fraud detection

• 99,000 normal transactions
• 1,000 fraud transactions

Why It's a Problem

Model learns to always predict majority class:

Predict "Not Fraud" for everything → 99% accuracy!
But catches 0% of actual fraud!

Solution 1: Oversampling

Add more minority class examples:

from imblearn.over_sampling import SMOTE

smote = SMOTE()
X_resampled, y_resampled = smote.fit_resample(X, y)

print(f"Original: {len(y)}")
print(f"After SMOTE: {len(y_resampled)}")

Solution 2: Undersampling

Remove majority class examples:

from imblearn.under_sampling import RandomUnderSampler

rus = RandomUnderSampler()
X_resampled, y_resampled = rus.fit_resample(X, y)

Solution 3: Class Weights

Tell model to care more about minority class:

from sklearn.ensemble import RandomForestClassifier

model = RandomForestClassifier(class_weight='balanced')
model.fit(X, y)

Solution 4: Different Metrics

Don't use accuracy! Use:

Precision: Of predicted frauds, how many were real?
Recall: Of real frauds, how many did we catch?
F1-Score: Balance of precision and recall

from sklearn.metrics import classification_report

predictions = model.predict(X_test)
print(classification_report(y_test, predictions))

Real Example - Credit Card Fraud

from imblearn.over_sampling import SMOTE
from sklearn.ensemble import RandomForestClassifier

# Apply SMOTE
smote = SMOTE()
X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)

# Train with class weights
model = RandomForestClassifier(class_weight='balanced')
model.fit(X_train_balanced, y_train_balanced)

When to Use What

• Lots of data: Undersample
• Little data: Oversample (SMOTE)
• Can't resample: Use class weights

Remember

• Never use accuracy alone
• Use F1-score or AUC
• Combine multiple techniques