Imbalanced-learn: Essential Toolkit for Handling Imbalanced Datasets

Overview

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Imbalanced-learn is a Python package offering a number of re-sampling techniques commonly used in datasets showing strong between-class imbalance. It is compatible with scikit-learn and is part of the scikit-learn-contrib projects.

Why Imbalanced-learn Matters

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In real-world datasets, it’s common to have imbalanced classes where one class significantly outnumbers others. This creates challenges:

• Credit Card Fraud: 99.9% legitimate transactions vs 0.1% fraudulent
• Medical Diagnosis: Rare diseases with few positive cases
• Manufacturing Defects: Most products pass quality control
• Customer Churn: Typically only 2-5% of customers churn

Standard machine learning algorithms often fail on imbalanced datasets, predicting only the majority class.