Imbalanced Data: Difference between revisions
Thakshashila (talk | contribs) Created page with "= Imbalanced Data = '''Imbalanced Data''' refers to datasets where the classes are not represented equally. In classification problems, one class (usually the positive or minority class) has far fewer examples than the other class (negative or majority class). == Why is Imbalanced Data a Problem? == Machine learning models often assume that classes are balanced and try to maximize overall accuracy. When data is imbalanced, models tend to be biased toward the majority..." |
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imbalanced data machine learning, handling imbalanced datasets, imbalanced classification problems, oversampling and undersampling, smote technique, cost-sensitive learning, evaluation metrics for imbalanced data | imbalanced data machine learning, handling imbalanced datasets, imbalanced classification problems, oversampling and undersampling, smote technique, cost-sensitive learning, evaluation metrics for imbalanced data | ||
[[Category:Artificial Intelligence]] | |||