SMOTE generates synthetic minority samples by interpolating between existing ones in feature space.

SMOTE generates synthetic minority samples by interpolating between existing ones in feature space. Appropriate for tabular numeric features with enough minority examples to interpolate. Not appropriate for text, very sparse features, or when minority samples represent true anomalies (don't synthesize fraud patterns). For tree models, class_weight often works just as well with less risk.