Replaces manual data balancing scripts with standardized re-sampling techniques for machine learning. It integrates with scikit-learn to handle class distribution issues using SMOTE and other algorithms.
Tech Stack
PythonAI, ML & Data
Why imbalanced-learn?
• Standardizes re-sampling methods
• Prevents data leakage in pipelines
• Wide variety of over/under-sampling
Limitations
• Can increase training time
• Risk of overfitting with over-sampling
• Requires understanding of sampling bias
3/5/2026
Last Update
1,327
Forks
61
Issues
MIT
License
Financial Leak Detected
Stop the "SaaS Tax"
Your team could be burning cash. Switching to imbalanced-learn instantly boosts your runway.
