What is Class Imbalance?
Class imbalance occurs when one class (the majority) in a dataset vastly outnumbers another (the minority). This is common in real-world scenarios like fraud detection or medical diagnosis. A standard model trained on such data becomes biased, learning to predict the majority class while ignoring the minority class, which is often the one we care about most.
The Accuracy Paradox
Imagine a dataset with 99% normal transactions and 1% fraudulent ones. A model that simply predicts "normal" for every case achieves 99% accuracy but is completely useless because it never detects fraud. This highlights why standard accuracy is a misleading metric for imbalanced problems.
Key Takeaway:
High accuracy doesn't mean a model is effective. We need better ways to measure performance and train our models when data is imbalanced.
Visualizing a 10:1 class imbalance.
Technique Explorer
This is an interactive environment to understand how different balancing techniques work. Select a method from the dropdown to see how it transforms an imbalanced dataset. The chart shows the original minority points (blue), original majority points (orange), and any new synthetic points (green).
Controls
Pros & Cons:
Choosing Your Strategy
There's no single best technique. The right choice depends on your data, model, and goals. Always start by establishing a baseline, use proper evaluation metrics, and experiment systematically.
A Recommended Workflow
Establish a Baseline
Train your model on the original, imbalanced data. This tells you if imbalance is truly the problem and gives you a score to beat.
Start Simple
Try Random Undersampling for very large datasets, or SMOTE for smaller ones. If your model supports it, test `class_weight='balanced'`. These often provide a solid improvement.
Advance if Needed
If simple methods aren't enough, explore hybrid methods like SMOTE-Tomek or specialized deep learning solutions like Focal Loss.
Essential Evaluation Metrics
| Metric | What it Measures | Primary Use Case |
|---|---|---|
| Accuracy | Overall correctness. | Misleading for imbalanced data. |
| Precision | Purity of positive predictions. | When False Positives are costly. |
| Recall / Sensitivity | Completeness of positive predictions. | When False Negatives are costly (e.g., medical diagnosis). |
| F1-Score | Harmonic mean of Precision and Recall. | A great general-purpose metric for balancing both. |
| AUC-PR | Area under the Precision-Recall curve. | Highly recommended for severe imbalance. |
| MCC | Correlation between predictions and true values. | A robust single score that considers all four confusion matrix cells. |