Imbalanced Datasets
Frequently Asked Questions
How do you handle imbalanced datasets?▼
By using resampling (oversampling minority or undersampling majority), adjusting loss weights, or using synthetic data algorithms like SMOTE.
Why is classification accuracy a poor metric for imbalanced datasets?▼
If 99% of data is negative, a model that classifies everything as negative is 99% accurate but useless. Metrics like F1-Score or AUC-ROC are preferred.
Quick Facts
- CategoryModel Training
- Key ApplicationSpam email database collection, transaction fraud database checking, and medical anomaly modeling.
Coverage Trend12 Weeks
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