Data Science
Handling Imbalanced Data
Senior Data Analyst
May 15, 2026
5 min read
Techniques
1. **Class Weights**: `class_weight='balanced'`
2. **Oversampling**: SMOTE
3. **Undersampling**: Random removal
4. **Metrics**: Use F1, Precision, Recall instead of Accuracy
from sklearn.utils.class_weight import compute_class_weight
model = RandomForestClassifier(class_weight='balanced')
*Day 136: Saving and Loading Models.*