SMOTE: Synthetic Minority Oversampling Technique

 SMOTE: Synthetic Minority Oversampling Technique

Synthetic Minority Over-sampling Technique, commonly referred to as SMOTE, is a preprocessing method used to address class imbalances in a dataset.

@raselahmed1337 : SMOTE

Benefits of SMOTE:

• Reduced risk of overfitting: SMOTE generates new synthetic samples instead of duplicating existing ones to mitigate the overfitting issue common with random oversampling. This makes SMOTE a superior alternative to random oversampling for imbalanced datasets.

• Improved results for imbalanced data: SMOTE is designed to address the challenges posed by imbalanced datasets and provides better outcomes compared to other methods such as random undersampling or oversampling. By creating synthetic samples that belong to the minority class, SMOTE ensures a more representative and balanced dataset for analysis.